AI Directory

Claude Haiku 4.5

Anthropic

Recommended

Anthropic's value baseline at $1/$5 per 1M tokens — the fast, low-cost tier rivals benchmark against, with 200K context and 64K output. Best for high-volume latency-sensitive work: classification, extraction, summarization, routing. Reach for Opus 4.8 or Sonnet 5 when deep multi-step reasoning is needed.

Input

$1 / 1M tokens

Output

$5 / 1M tokens

Context

200K ctx; legacy, Haiku 5.5 is current

Claude Opus 4.8

Anthropic

Recommended

Claude Opus 4.8 is Anthropic's previous-generation Opus, now listed as legacy: 1M-token context, 128K output, $5/$25 per 1M tokens, retirement not before 28 May 2027. It remains our pick for review-heavy work such as code review and constrained refactoring, where deliberate pacing beats persistence. New workloads should start on Claude Opus 5.5 at $4/$20, which Anthropic now recommends.

Input

$5 / 1M tokens

Output

$25 / 1M tokens

Context

1M tokens

Claude Opus 5

Anthropic

Recommended

Claude Opus 5 delivers near-Fable 5 intelligence at $5/$25 per 1M tokens, with a 1M-token context and 128K output. Anthropic now lists it as legacy: still available, retirement not before 24 July 2027, and Claude Opus 5.5 ($4/$20) is its recommended starting point. Recommended for teams already running Opus 5; new workloads should start on Opus 5.5, which is cheaper per token.

Input

$5.00 / 1M tokens

Output

$25.00 / 1M tokens

Context

1M ctx; 128K out; legacy; fast $10/$50

Claude Sonnet 5

Anthropic

Recommended

Anthropic's most agentic Sonnet at launch delivers performance close to Opus 4.8 at 40 percent of the Opus rate. The $2/$10 per 1M token price is the standard rate: Anthropic cancelled the increase to $3/$15 that was scheduled for 1 September 2026. The newer tokenizer produces about 30 percent more tokens for the same text, so recount budgets before migrating from Sonnet 4.6. Anthropic now lists Sonnet 5 as legacy; Claude Sonnet 5.5, at the same $2/$10, is the current Sonnet, so start new work there.

Input

$2 / 1M

Output

$10 / 1M

Context

1M tokens; batch $1/$5; cache read $0.20

DeepSeek V4 Flash

DeepSeek

Recommended

DeepSeek retired V4 Flash from its API on 10 September 2026: the deepseek-v4-flash id is still accepted, but its requests are served by V4.1-Flash and billed at the Flash rate of $0.15 input and $0.60 output per 1M tokens off-peak. The MIT-licensed weights (284B parameters, 13B active, 1M-token context) remain on Hugging Face, so this is now a frozen checkpoint for self-hosting and reproducible evals. For new API work, call deepseek-flash.

Input

$0.15 / 1M tokens

Output

$0.60 / 1M tokens

Context

Retired Sep 10; id serves V4.1-Flash. Peak 2x M-F

DeepSeek V4 Pro

DeepSeek

Recommended

Strong price-performance on open MIT weights; the 2x peak window is 01:00-04:00 and 06:00-10:00 UTC on weekdays only, so weekends bill entirely off-peak.

Input

$0.66 / 1M tokens

Output

$1.98 / 1M tokens

Context

1M tokens; 2x price at weekday peak hours

Gemini 2.5 Pro

Google

Recommended

Now the budget long-context pick: a 40% price cut (Jun 2026) made full-corpus passes affordable at mid-size budgets. It offers the longest context window available, with strong Google ecosystem integration.

Input

$1.25 / 1M tokens

Output

$10 / 1M tokens

Context

1M tokens

Gemini 3.5 Flash

Google

Recommended

Google's cost-efficiency standout for agentic desktop automation at $1.50/$9 per 1M tokens with ~1M context. Native Computer Use scores 78.4 on OSWorld-Verified — within 0.3 points of GPT-5.5 at roughly one-third the cost. THE relevant caveat: the Computer Use tool is a public preview not yet in the production API, so verify endpoint availability before wiring in.

Input

$1.50 / 1M tokens

Output

$9 / 1M tokens

Context

1M tokens

Gemini 3.5 Flash-Lite

Google

Recommended

Gemini 3.5 Flash-Lite is Google's fastest, cheapest 3.5-series model at 350 tok/s and $0.30/$2.50. The generational leap from 3.1 Flash-Lite is the real story — it dramatically outperforms its predecessor on key agentic benchmarks and beats the full-size Gemini 3 Flash on coding and computer-use tasks. For developers scaling high-throughput agentic subagents, search pipelines, and document processing, Flash-Lite's price-to-performance ratio is unmatched in Google's lineup.

Input

$0.30 / 1M

Output

$2.50 / 1M

Context

1M context, 64K output, 350 tok/s

Gemini 3.6 Flash

Google

Recommended

Gemini 3.6 Flash is Google's new default Flash-tier workhorse: better than 3.5 Flash on every benchmark and cheaper, at $0.75/$3.75 per 1M tokens through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01. The 17% token-efficiency gain lowers per-task cost, and built-in Computer Use via the API makes it a credible agentic coding option. For Google-ecosystem developers it is the clear upgrade; budget for the January rate step.

Input

$0.75 / 1M (through 2026-12-31)

Output

$3.75 / 1M (through 2026-12-31)

Context

1M context, 64K output; $1.50/$7.50 from 2027-01-01

Gemini 3.7 Flash

Google

Recommended

Gemini 3.7 Flash is Google's strongest Flash workhorse yet for coding and agents — a genuine step up from 3.6 Flash at half the prior price through 2026. It still trails the frontier on the hardest terminal and computer-use tasks. A clear upgrade for cost-sensitive and Google-ecosystem agent builders; not a frontier-code replacement.

Input

$0.75 / 1M

Output

$3.75 / 1M

Context

1M context, 64K output

Gemini 3.8 Flash

Google

Recommended

Gemini 3.8 Flash is Google's strongest Flash workhorse yet — a genuine capability upgrade over the recommended 3.7 Flash at the same introductory price, with real gains across software engineering, agents and professional domains. The deciding caveat is Google's own: 3.8 "works harder," so per-task token burn runs well above 3.7, which remains the efficiency-first pick. Adopt 3.8 for long-horizon coding, agents and finance or legal work — benchmark your own workload before the intro price expires.

Input

$0.75 / 1M

Output

$3.75 / 1M

Context

1M context, 64K output

Gemini 3.8 Live Extended Thinking

Google

Recommended

Gemini 3.8 Live Extended Thinking is the voice-agent model to start with: it tops Artificial Analysis' Speech to Speech Index and reasons and calls tools in the background while it keeps talking, at the same rate as the default Gemini 3.8 Live. The caveat is the Live API surface: no structured outputs, caching, code execution or batch. Right for teams building real-time voice agents that must solve multi-step problems mid-call.

Input

$0.75 / 1M (text)

Output

$4.50 / 1M (text)

Context

131K in, 65K out; audio $3/$12 per 1M

GPT-4.1

OpenAI

Recommended

The default migration target for GPT-4 Turbo workloads: same request shape, lower cost, 1M-token context — convert legacy function_call payloads to the tools format and move on.

Input

$2 / 1M tokens

Output

$8 / 1M tokens

Context

1M tokens

GPT-5.5

OpenAI

Recommended

GPT-5.5 is OpenAI's April 2026 general-purpose model at $5 input and $30 output per 1M tokens, with a 1M+ token context (922K in, 128K out). It is no longer OpenAI's only deployable frontier option: the GPT-5.6 family is on the public price list, and GPT-6 Sol lists at $2/$10. It stays recommended as a stable, widely supported production baseline, but new workloads should test GPT-6 Sol or GPT-5.6 Terra first.

Input

$5 / 1M tokens

Output

$30 / 1M tokens

Context

1M+ tokens (922K in / 128K out)

GPT-5.6 Sol

OpenAI

Recommended

GPT-5.6 Sol is no longer OpenAI's best-value frontier model: its successor GPT-6 Sol, released 22 September 2026, lists at $2/$10 per 1M tokens against Sol's $4/$20. It stays recommended because its coding lead has third-party backing, while every GPT-6 Sol score so far is OpenAI's own. OpenAI lists no deprecation, and the $4/$20 rate is promotional through at least 21 November 2026. Test GPT-6 Sol before committing new workloads.

Input

$4 / 1M tokens

Output

$20 / 1M tokens

Context

Short $4/$20 · long $8/$30 per 1M · promo

GPT-Live-Transcribe

OpenAI

Recommended

OpenAI's low-latency streaming speech-to-text model delivers live transcript deltas at $0.017/min with tunable latency and context-aware transcription. It improves on gpt-realtime-whisper's error rate and supports real-time keyword hints, language hints, and mid-session configuration updates via WebSocket or WebRTC. The tradeoff: ~3.8x the cost of batch GPT-Transcribe, and no diarization, word timestamps, or confidence scores. Best for live captioning, meeting assistants, and voice agents where sub-second transcript display matters more than per-minute cost.

Input

$0.017 / minute

Output

N/A

Context

Per-minute realtime audio billing

GPT-Transcribe

OpenAI

Recommended

OpenAI's latest batch speech-to-text model scores 3.31% AA-WER at $0.0045/min — 25% cheaper and measurably more accurate than its gpt-4o-transcribe predecessor. It supports context prompting, keyword hints, and language detection across 22+ languages, making it the best default for asynchronous file transcription on the OpenAI platform. Caveat: no diarization or word-level timestamps; use gpt-4o-transcribe-diarize for speaker labels or gpt-live-transcribe for real-time needs. Best for developers building meeting transcription, media processing, and voice interfaces on the OpenAI API.

Input

$0.0045 / minute

Output

N/A

Context

Per-minute audio billing

Claude Fable 5

Anthropic

Conditional

Anthropic's Mythos-class frontier model, the strongest publicly available Claude at its June 2026 launch, now listed as legacy behind Claude Fable 5.1. The verdict stays conditional on two constraints: API pricing of $10/$50 per 1M tokens, and a safety classifier that over-triggered on routine coding tasks at launch, routing them to an Opus 4.8 fallback. Subscribers reach Fable through Claude Max at 50% of weekly limits, or through usage credits on Pro. Anthropic's current lineup lists Fable 5.1 and Opus 5.5 for new work.

Input

$10 / 1M tokens

Output

$50 / 1M tokens

Context

1M ctx, 128K out; legacy; Max plans at 50% limits

Claude Fable 5.1

Anthropic

Conditional

Anthropic's new frontier flagship is the most capable model publicly available, and it largely fixes the safeguard over-triggering that made Fable 5 frustrating for routine coding. The verdict stays conditional on cost: independent measurement shows more spend per task than Fable 5 despite the cheaper cache reads, because the model thinks longer. Reach for it on demanding reasoning and long-horizon agentic work. For everyday coding, Claude Opus 5.5 at $4/$20 per 1M is Anthropic's recommended starting model and the better default.

Input

$10 / 1M tokens

Output

$50 / 1M tokens

Context

1M context, 128K output; cache $0.25/1M

Claude Haiku 5.5

Anthropic

Conditional

Claude Haiku 5.5 is a conditional pick: Anthropic's fastest and cheapest current Claude at $0.10 input and $0.50 output per 1M tokens, with a 1M context and adjustable effort. Prompts over 100K tokens cost five times as much, and every benchmark is Anthropic's own. Right for high-volume classification, extraction, routing and subagent work. Use Sonnet 5.5 for complex agentic coding, where Anthropic's own Terminal-Bench 4.0 numbers put it well ahead.

Input

$0.10 / 1M tokens

Output

$0.50 / 1M tokens

Context

1M ctx; prompts over 100K cost $0.50/$2.50

Claude Mythos 5.1

Anthropic

Conditional

Same weights as Claude Fable 5.1 at the same $10/$50 rate, with cyber and life-sciences safeguards loosened for vetted users. The catch is access: Mythos 5.1 ships only through Anthropic's trusted-access programs (Cyber Verification and Life Sciences Verification), so there is no self-serve API and no way to buy your way in. Conditional for the same reason as Mythos 5: elite capability behind a vetting gate. Everyone else should use Fable 5.1.

Input

$10 / 1M tokens

Output

$50 / 1M tokens

Context

1M context, $0.25/1M cache; vetted access

Claude Opus 5.5

Anthropic

Conditional

Claude Opus 5.5 is the model Anthropic now recommends starting with for most workloads, built for long-running agentic coding and knowledge work at rates 20% below Claude Opus 5. Conditional because every benchmark is Anthropic's own and migration is not drop-in: thinking cannot be turned off, forced tool use returns an error, and most cybersecurity tasks are rerouted to Opus 4.8. Right for Claude Opus 5 teams that can absorb those breaking changes.

Input

$4 / 1M tokens

Output

$20 / 1M tokens

Context

1M ctx; 128K out; cache $0.20; fast $8/$40

Claude Sonnet 4.6

Anthropic

Conditional

Superseded by Claude Sonnet 5 (shipped Jun 30, 2026), which Anthropic now lists as legacy behind Claude Sonnet 5.5 as the current Sonnet. Both list at $2/$10 per 1M tokens against Sonnet 4.6's $3/$15, since Anthropic cancelled the scheduled rise to $3/$15. Sonnet 5 also adds a 13.4-point Terminal-Bench 2.1 jump. Choose 4.6 only for pinned deployments or where Sonnet 5's ~30% higher token count and removed temperature/top_p/top_k are disqualifying.

Input

$3 / 1M tokens

Output

$15 / 1M tokens

Context

1M tokens

Claude Sonnet 5.5

Anthropic

Conditional

Conditional. Claude Sonnet 5.5 is Anthropic's default Sonnet model on the Anthropic API, on the same rate card as Sonnet 5, and Anthropic says it runs faster and costs less per task. Every benchmark is Anthropic's own, and five breaking changes hit code written for Sonnet 5. Right for teams ready to re-test agentic coding and knowledge work; hold off if you rely on forced tool choice or temperature control.

Input

$2 / 1M tokens

Output

$10 / 1M tokens

Context

1M ctx; batch $1/$5; cache read $0.10

DeepSeek V4.1 Flash

DeepSeek

Conditional

DeepSeek V4.1 Flash, the model now behind DeepSeek's deepseek-flash API, is a strong-value open-weight pick: MIT-licensed weights, image input, a 1M-token context and an Artificial Analysis score well above average for comparable models. Conditional because its standout coding-agent results are DeepSeek's own, Artificial Analysis measured it as very verbose, and peak hours double the rate. Right for cost-sensitive pipelines that can run off-peak or self-host; wrong for buyers who need independently verified agent performance before committing.

Input

$0.15 / 1M tokens

Output

$0.60 / 1M tokens

Context

1M tokens; 2x price at weekday peak hours

Conditional. Eleven v4 is ElevenLabs' long-form text-to-speech model (id eleven_v4), covering 90+ languages with multi-speaker dialogue and voice cloning, and accepting up to 10,000 characters per request. The deciding caveat is price: $0.022 per 1K characters is a 72%-off promo that ends Oct 12 against a $0.08 list rate, and the quality claims are ElevenLabs' own, untested by us. Suits teams producing content and long-form audio who budget at list price.

Input

$0.08 / 1K characters

Output

Billed per character (single rate)

Context

List $0.08 / 1K chars; 10K char limit

Conditional. Eleven v4 Turbo (id eleven_v4_turbo) is ElevenLabs' real-time speech model, stating a median inference latency of about 100 ms, excluding application and network latency, across 90+ languages via a Text to Dialogue WebSocket. The deciding caveat is that the latency figure is vendor-stated and untested by us, and $0.011 per 1K characters is a promo ending Oct 12 against a $0.04 list rate. Suits builders of voice agents and interactive characters.

Input

$0.04 / 1K characters

Output

Billed per character (single rate)

Context

List $0.04 / 1K chars; median latency ~100 ms

FLUX 3

Black Forest Labs

Conditional

FLUX 3 is Black Forest Labs' unified multimodal model for image, video, audio and robot-action prediction. Its Video component is now pay-as-you-go through BFL's dashboard and API, from $0.06 per second at Draft HD to $0.80 at UHD, with five seconds of Standard HD costing $0.85. Conditional because every preference result is BFL's own, Image and Audio are not yet released, and the open-weight FLUX 3 Dev is still listed as coming soon.

Input

$0.17 / sec (Standard HD text-to-video)

Output

Included in per-second video rate

Context

Video GA pay-as-you-go; $0.06-$0.80/s by res

Gemini 3.1 Pro

Google

Conditional

Gemini 3.1 Pro is Google's most capable publicly available Pro-tier model, delivering strong reasoning with a 1M-token context window at competitive pricing — and it's the migration target for GitHub Copilot users leaving deprecated Gemini 2.5 Pro today. But it's decidedly previous-gen: Gemini 3.6 and 3.5 Flash now beat it on coding and agentic benchmarks at lower cost. Stick with it if you're deep in the Google Cloud / Vertex AI ecosystem; otherwise, the Flash line or competitors deliver more for less.

Input

$2 / 1M

Output

$12 / 1M

Context

1M tokens

Gemini 3.8 Flash Cyber

Google

Conditional

Gemini 3.8 Flash Cyber is Google's most capable cybersecurity model, a defensive specialist already in production at Chrome Security, Cloud Vulnerability Research and partner Wiz, with frontier vulnerability-discovery and patching results at a fraction of the leaders' rollout cost. The deciding caveat is access: it is gated to vetted defenders through the Fairwind Program, with no public API or published rate card. We rate it conditional rather than caution like the sibling Gemini 3.5 Flash Cyber because 3.8 ships published benchmark figures and named production deployments where 3.5 had neither, even though both remain gated. Right for organizations that can qualify as trusted defenders; everyone else cannot buy it today.

Input

N/A (no public rate card)

Output

N/A (no public rate card)

Context

Fairwind-gated; no public API

Gemini 3.8 Flash TTS

Google

Conditional

Conditional. Gemini 3.8 Flash TTS is Google's flagship text-to-speech model, available in the Gemini API with voice design, consented voice replication and SynthID watermarking on every clip. The deciding caveat is cost: today's paid rates are launch prices that double when the launch window closes, and its Hume AI leaderboard placements are Google-reported, not measured by us. Suits developers building expressive narration, character voices or dubbing who can budget for the post-launch rate.

Input

$0.50 / 1M (text)

Output

$9.00 / 1M (audio)

Context

Launch rate to 2026-12-31; $1/$18 from 2027

Gemini 3.8 Flash-Lite TTS

Google

Conditional

Conditional. Gemini 3.8 Flash-Lite TTS is Google's cost tier for high-volume speech: stable in the Gemini API, built into Google Vids, and billed at the same text input rate as Flash TTS with a lower audio output rate. Google ranks it second only to Flash TTS on Hume AI's Overall Quality Index, but that ranking is Google-reported and its launch rates are temporary, doubling once the promotion ends. Suits voice-agent cascades and bulk narration where cost per clip matters more than acting range.

Input

$0.50 / 1M (text)

Output

$6.00 / 1M (audio)

Context

Launch rate to 2026-12-31; $1/$12 from 2027

Gemini 3.8 Live

Google

Conditional

Gemini 3.8 Live is Google's default Live API model for low-latency voice agents that keep talking while tool calls finish in the background. Conditional because Google bills Gemini 3.8 Live Extended Thinking at the identical rate, and Artificial Analysis scores that sibling higher on its Speech to Speech Index, so you pay the same for less reasoning. Pick 3.8 Live only when turn latency matters more than reasoning depth: developers shipping real-time voice experiences on the Gemini API.

Input

$0.75 / 1M (text)

Output

$4.50 / 1M (text)

Context

131K in, 65K out; audio $3/$12 per 1M

GLM-5.2

Z.ai

Conditional

Top open-weight coding model with 1M-token context and MIT license. It beats GPT-5.5 on multiple benchmarks at a fraction of cost, but still trails Claude Opus 4.8 on the hardest long-horizon tasks.

Input

$1.40 / 1M tokens

Output

$4.40 / 1M tokens

Context

1M tokens

GLM-5.3

Z.ai

Conditional

GLM-5.3 is Z.ai's open-weights coding and cyber-defense model on the GLM-5.2 base, with every gain from post-training. Pricing is published at $1.40 input / $4.40 output per 1M, the same as GLM-5.2, and the weights are now on Hugging Face. It stays conditional because its headline coding and cyber scores come from Z.ai's own evaluations: reach for defensive security and agentic automation, but audit it yourself before standardizing.

Input

$1.40 / 1M tokens

Output

$4.40 / 1M tokens

Context

1M ctx; Flash $0.15/$0.50, launch promo ended

GPT-4o (Retired from ChatGPT, API-only)

OpenAI

Conditional

GPT-4o (May 2024) has been superseded by the GPT-5.x family and delisted from OpenAI's flagship API pricing page. It remains available through the API but is no longer a current-generation model. Use only where migration to GPT-5.x is blocked; otherwise, GPT-5.5 or GPT-5.4 offer better performance at comparable cost.

Input

$2.50 / 1M tokens

Output

$10 / 1M tokens

Context

128K tokens

GPT-5.5-Cyber

OpenAI

Conditional

85.6% CyberGym — top single-model score — but restricted-access for vetted defenders only.

Input

N/A

Output

N/A

Context

Trusted Access for Cyber; not commercial API

GPT-5.6 Luna

OpenAI

Conditional

GPT-5.6 Luna is OpenAI's previous-generation budget model at $0.20 input and $1.20 output per 1M tokens short context, generally available through the API and Codex. Its successor GPT-6 Luna, released 22 September 2026, lists at $0.10/$0.50, so Luna is no longer the low-cost default for new OpenAI work. It remains conditional because benchmarks are vendor-reported and it lacks activation classifiers; OpenAI lists no deprecation, so existing pipelines need not migrate yet.

Input

$0.20 / 1M tokens

Output

$1.20 / 1M tokens

Context

Short $0.20/$1.20 · long $0.40/$1.80 per 1M

GPT-5.6 Terra

OpenAI

Conditional

GPT-5.6 Terra is OpenAI's mid-tier workhorse: GPT-5.5-class capability on OpenAI's own claim, at $2.00 input and $12.00 output per 1M tokens at short context ($4.00 / $18.00 long) after the 20% price cut of 30 July 2026. It is generally available via the API and Codex. Remains conditional: all benchmarks are vendor-reported without independent reproduction, and Terra is not selectable in the standard ChatGPT model picker.

Input

$2.00 / 1M tokens

Output

$12.00 / 1M tokens

Context

Short $2.00/$12.00 · long $4.00/$18.00 per 1M

GPT-5.6-Cyber

OpenAI

Conditional

GPT-5.6-Cyber is OpenAI's purpose-trained cybersecurity model, built on GPT-5.6 Sol and available exclusively through the gated Daybreak Red program. It is the first model to reach OpenAI's 'High' cyber capability threshold under its Preparedness Framework, with exploit-chain and zero-day performance that far exceeds general-purpose models. Reach for it if you are an authorized red team or vulnerability researcher; everyone else should use GPT-5.6 Sol via Daybreak Blue.

Input

$12.50 / 1M tokens

Output

$75 / 1M tokens

Context

400K tokens

GPT-6 Astra

OpenAI

Conditional

Astra ships at $10/$50 per 1M tokens short context, $20/$75 long context, with a 1,050,000-token context window and 128K max output. On OpenAI's own reported benchmarks it leads on computer use, terminal work, long context and cyber. We previously placed it third on the Artificial Analysis Intelligence Index at 61.2; we could not source that figure and have withdrawn it, and Artificial Analysis publishes no Astra score, so we have no vendor-neutral general-intelligence ranking for it. Conditional on two constraints: Fast mode is unavailable under EU data residency, and OpenAI disclosed decreased monitorability versus GPT-5.6 Sol. Route to it by task; do not make it your default.

Input

$10 / 1M tokens

Output

$50 / 1M tokens

Context

1.05M tokens; short-ctx standard rate

GPT-6 Luna

OpenAI

Conditional

GPT-6 Luna is OpenAI's new budget model and the cheapest way into GPT-6, at half GPT-5.6 Luna's input rate, with a 1.05M-token context and text and image input. Conditional because every published result is OpenAI's own and prompts over 272K input tokens bill at higher rates. GPT-5.6 Luna stays on the price list with no deprecation notice, so migration is optional. Right for teams running high-volume, everyday API work at scale.

Input

$0.10 / 1M tokens

Output

$0.50 / 1M tokens

Context

1.05M ctx; over 272K $0.20/$0.75; cache $0.01

GPT-6 Sol

OpenAI

Conditional

GPT-6 Sol is the lower-priced companion to GPT-6 Astra and succeeds GPT-5.6 Sol at half its per-token rate, with a 1.05M-token context and 128K output. Conditional because every published score is OpenAI's own and prompts over 272K input tokens bill at higher rates. GPT-5.6 Sol stays on the price list with no deprecation notice, so nothing forces a migration. Right for teams routing hard coding and agent work to OpenAI.

Input

$2 / 1M tokens

Output

$10 / 1M tokens

Context

1.05M ctx; over 272K $4/$15; cache $0.20

GPT-6.1 Sol

OpenAI

Conditional

Conditional on evidence: GPT-6.1 Sol is OpenAI's model for complex coding, computer use and professional work, which OpenAI itself describes as near-Astra performance at a lower cost. We could not read the launch post, so no benchmark is stored and the quality claim is OpenAI's alone. Prompts past 272K input tokens bill at higher rates, and reasoning cannot be set to none or minimal. Suits teams building agents on the Responses API who will run their own evals first.

Input

$2 / 1M tokens

Output

$10 / 1M tokens

Context

1.05M ctx; over 272K $4/$15; cache $0.10

GPT-Live-1

OpenAI

Conditional

GPT-Live-1 is OpenAI's full-duplex voice model: it listens and speaks at the same time and hands reasoning and tool use to a backend model. Since 10 September 2026 it is generally available in the API on the Live endpoint at $0.05 per minute, with backend model and tool usage billed separately. Conditional because its benchmarks are OpenAI's own, language quality is uneven, and real cost per call depends on the backend model you pair it with.

Input

$0.05 / minute (voice session)

Output

Included in per-minute session rate

Context

Live API; backend model + tools billed separately

GPT-Live-1 mini

OpenAI

Conditional

The free-tier variant of GPT-Live-1 — same full-duplex architecture in a smaller, faster package, serving as the default voice model for ChatGPT Free users. Shares the core innovations: continuous interaction, wake word support, and GPT-5.5 delegation. Same limitations apply: no API, uneven language quality. A substantial upgrade over the old turn-based voice mode for free users.

Input

Free (ChatGPT)

Output

Free (ChatGPT)

Context

ChatGPT Voice only

Grok 4.5

SpaceXAI

Conditional

Competitively priced coding and agentic model at $2/$6 per 1M tokens up to 200K tokens of context, doubling to $4/$12 above that, with a 500K window. xAI's published benchmarks place it mid-pack behind Fable 5 and GPT-5.5, though it leads Opus 4.8 on DeepSWE and Terminal-Bench 2.1. The real story is cost efficiency: 4.2x more token-efficient than Opus 4.8 on SWE-Bench Pro. Best for teams needing strong coding at a fraction of frontier pricing; xAI's docs list only US regions.

Input

$2 / 1M tokens

Output

$6 / 1M tokens

Context

500K ctx; 200K+ $4/$12; cache $0.30 (200K+ $0.60)

Grok 4.6

SpaceXAI

Conditional

Grok 4.6 is SpaceXAI's frontier model for coding and agentic work at $2 input and $6 output per 1M tokens under 200K, with a 500K-token context; prompts of 200K or more bill at $4/$12. At launch it scored 61 on the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol, but that is still one independent evaluator. Grok 4.7 now lists at the same price, so compare the two before committing new work.

Input

$2.00 / 1M tokens

Output

$6.00 / 1M tokens

Context

500K ctx; 200K+ $4/$12; cache $0.50

Grok 4.7

SpaceXAI

Conditional

Grok 4.7 keeps Grok 4.6's price and speed, and xAI says it works longer on hard tasks and checks its own work more carefully. Conditional because every published score is xAI's own, prompts of 200K tokens or more bill at double the headline rate, and the Batch API is not supported. Right for coding teams already working in Cursor or Grok Build.

Input

$2.00 / 1M tokens

Output

$6.00 / 1M tokens

Context

500K ctx; 200K+ $4/$12; fast variant 2x price

Grok Voice 3.0

SpaceXAI

Conditional

Grok Voice 3.0 is SpaceXAI's speech-to-speech model for real-time voice agents, priced at $0.08 per minute, the same as Grok Voice 2.0. Artificial Analysis shows the highest Speech-to-Speech Index score, 86.9, with 0.81 seconds to first audio, but xAI's other benchmark and customer figures are its own. Conditional because we have not tested it and the grok-voice-latest alias moved to it automatically. It suits teams building phone or in-app voice agents on a managed model.

Input

$0.08 / min of audio (speech-to-speech)

Output

Included in per-minute audio rate

Context

Voice Agent API; $4.80 / hr

Grok Voice Think Fast 2.0

SpaceXAI

Conditional

Grok Voice Think Fast 2.0 is SpaceXAI's next-generation speech-to-speech model with a parallel reasoning architecture that makes it substantially smarter than conventional voice models with no latency penalty. Its transcription accuracy destroys dedicated STT competitors in noisy environments — the deciding factor for production voice agents. But at $0.08/min, developer/API-only with no consumer-facing assistant, it's a premium specialist — not for cost-sensitive or general-purpose voice use.

Input

$0.08 / min of audio (speech-to-speech)

Output

Included in speech-to-speech rate

Context

API-only

Grok Voice Transcribe 2.0

SpaceXAI

Conditional

Grok Voice Transcribe 2.0 is SpaceXAI's speech-to-text model at $0.10 per hour of audio in batch, with speaker labels, word timestamps and key terms included. Conditional because every accuracy figure is SpaceXAI's own: it says the model is twice as accurate as 1.0 and first among 32 streaming models on Artificial Analysis, and we could not check that leaderboard. Right for voice-agent and call-transcription developers who will test it on their own audio; we have not tested it.

Input

$0.10 / hour of audio (batch)

Output

Included in per-hour audio rate

Context

Streaming $0.20 / hour of audio

Hy3

Tencent

Conditional

Apache 2.0 open-weight MoE agentic model (295B total, 21B active) with strong tool-calling reliability and anti-hallucination behavior, but coding trails GLM-5.2 and self-hosting demands 8+ GPUs. The free OpenRouter tier that ran through Jul 21 is gone; OpenRouter now lists six providers at $0.13-$0.20 input and $0.53-$0.80 output per 1M tokens. Best for self-hosted coding agents and long-context document work where permissive licensing matters more than absolute benchmark scores.

Input

$0.20 / 1M

Output

$0.80 / 1M

Context

256K tokens; OpenRouter providers $0.13-$0.20 in / $0.53-$0.80 out; free tier ended

Inkling

Thinking Machines Lab

Conditional

Inkling is the strongest reason yet to take open-weight multimodal AI seriously. It delivers competitive reasoning and coding with best-in-class open-weight safety and controllable thinking effort, all under Apache 2.0. But Thinking Machines is honest that it's not the strongest overall — factuality lags (SimpleQA 43.9%) and self-hosting demands serious hardware (2TB VRAM). Conditional: a compelling base for organizations that need to own and fine-tune a multimodal model, not for teams wanting peak off-the-shelf performance.

Input

$1.87 / 1M tokens

Output

$4.68 / 1M tokens

Context

Up to 1M tokens native; Tinker 64K and 256K tiers; rates at 50% limited-time discount (list $3.74 / $9.36)

Kimi K3

Moonshot AI

Conditional

Moonshot AI's open-weight MoE model (Kimi K3 License, weights on Hugging Face) offers a 1M-token context at $3/$15 per 1M tokens and holds #1 on Arena WebDev (preliminary), but still trails Fable 5 and GPT-5.6 Sol on precise execution, and serving the weights needs 64+ accelerators. The rating stays conditional on legal risk: on July 22, 2026 the White House OSTP accused Moonshot of distilling Fable 5 and Treasury threatened sanctions. We have not re-verified that dispute's status this run.

Input

$3.00 / 1M

Output

$15.00 / 1M

Context

1M ctx, $0.30 cached. Weights live (Jul 27).

Kolibri-1 is Aleph Alpha's English-German mixture-of-experts model with Apache 2.0 weights on Hugging Face, and a credible open-weight option for teams that must run German-language workloads on their own hardware. The catch is operations: the FP8 weights need about 78 GB of GPU memory, the pages we read name no hosted API or price, and every benchmark comes from Aleph Alpha's own harnesses. Right for public-sector and industrial teams that can self-host. Skip it for hosted-API needs or other languages.

Input

Free (open weights)

Output

Free (open weights)

Context

262K native, 1M extrapolated; ~78 GB FP8

Laguna S 2.1

poolside

Conditional

Laguna S 2.1 is the first credible Western open-weight coding model in 11 months — a 118B MoE that activates only 8B parameters per token yet scores 70.2% on Terminal-Bench 2.1, beating models 10x its size. But it's not at the frontier — GPT-5.6 Sol and Kimi K3 sit far ahead — and known limitations include harness overfitting, JSON mangling in nested tool arguments, and no configurable thinking-effort control. Poolside's collapsed $14B Series C raises sustainability questions, making this best for self-hosted agentic coding where data sovereignty matters more than absolute benchmark leadership.

Input

$0.10 / 1M

Output

$0.20 / 1M

Context

OpenMDW-1.1 license, 1M context

Llama 4 Maverick

Meta

Conditional

The leading open-source model for teams that need self-hosting, data sovereignty, or want to avoid API vendor lock-in.

Input

Free (self-hosted) or $0.20 / 1M tokens (hosted)

Output

Free (self-hosted) or $0.60 / 1M tokens (hosted)

Context

1M tokens

MAI-Code-1-Flash

Microsoft

Conditional

MAI-Code-1-Flash was Microsoft's first in-house coding model, a sparse MoE priced at $0.75/$4.50 per 1M tokens. GitHub retired it from Copilot on 10 September 2026 and names MAI-Code-1.1-Flash, at $0.20/$1.20, as the replacement; it no longer appears in Copilot's supported-models or pricing tables. Copilot users should switch to 1.1-Flash, which is cheaper and scores higher on Terminal-Bench 2.1.

Input

$0.75 / 1M tokens

Output

$4.50 / 1M tokens

Context

Retired from Copilot Sep 10 2026; 256K ctx

MAI-Code-1.1-Flash

Microsoft

Conditional

MAI-Code-1.1-Flash is Microsoft's refreshed small-tier Copilot coding model — a quarter the cost and 22% better on Terminal-Bench 2.1 than its June predecessor, now with native vision. It's built for high-volume iterative work rather than frontier reasoning, making it the cheapest serious coding model in Copilot. Reach for it on everyday completions, refactors, and image-assisted debugging; use a flagship for complex architecture.

Input

$0.20 / 1M

Output

$1.20 / 1M

Context

256K context

MAI-Cyber-1-Flash

Microsoft

Conditional

Microsoft's first in-house cybersecurity model, built on the MAI-Thinking-1 family and embedded inside the MDASH multi-agent harness, handles about 90% of security workloads at roughly half the cost of prior configurations and reserves GPT-5.4 for the hardest 10%. Microsoft reports 96% on CyberGym by the any-crash metric and 86.3% by the stricter final-submission metric, both from a tuned full-system configuration rather than an independent model-versus-model test. Available only through Azure AI Foundry with vetted enterprise access; no standalone pricing or public API.

Input

N/A (MDASH SCU consumption)

Output

N/A (MDASH SCU consumption)

Context

Azure AI Foundry vetted access

Conditional. Mercury 2.5 is Inception's diffusion LLM, and raw output speed is the reason to pick it: Artificial Analysis ranks it among the fastest models it measures, and it ships tunable reasoning, parallel tool calls and structured outputs. The deciding caveat is quality: Artificial Analysis places its intelligence just below the median, and Inception's parity claims name no benchmark. Suits latency-bound voice agents, RAG pipelines and coding subagents, not hard reasoning.

Input

$0.20 / 1M tokens

Output

$0.75 / 1M tokens

Context

260K ctx; 80% off at launch: $0.04/$0.15

Mercury 3 is Inception's diffusion LLM with a 1M-token context at a list price of $0.20 input and $0.70 output per 1M tokens, and speed is the reason to pick it. The deciding caveat is that its quality claims come from Inception and its partners, and the headline comparisons name no benchmark. Suits voice, chat, search and browser agents where latency sets the experience, not hard reasoning.

Input

$0.20 / 1M tokens

Output

$0.70 / 1M tokens

Context

1M ctx; list rate shown; 50% off at launch ($0.10 / $0.35), no end date stated, so plan on the list rate

MiniMax M3.1 Flash is a 1M-context multimodal coding and agent model at $0.15 input and $0.60 output per 1M tokens, and MiniMax says its agentic usage cost is 70% lower than its predecessor's. The deciding caveat is evidence: the launch post's scores are MiniMax's own, it names no comparison model, and the weights are promised only for later. Worth a trial for cost-sensitive agent and computer-use workloads, not yet for anyone who needs self-hosting or independent scores.

Input

$0.15 / 1M tokens

Output

$0.60 / 1M tokens

Context

1M context; cached input $0.015 / 1M

Mistral Large 4

mistral

Conditional

Conditional: Mistral Large 4 is a public preview, not a finished release, since Mistral says its reinforcement learning run is still in flight and promises weights "by the end of the month". It is a mixture-of-experts model of about 1 trillion parameters, 52B active, with a 1M context on its docs page and a $1.36 input, $4.18 output list price per 1M tokens. Most benchmarks are Mistral's own. Trial it now if you want an open-weight-bound model, but wait for weights and licence before committing.

Input

$1.36 / 1M tokens

Output

$4.18 / 1M tokens

Context

1M ctx; preview; sale $0.68/$2.09 on docs

Muse Glimmer

Meta

Conditional

Muse Glimmer is Meta's 30B open-weight agentic model, distilled from the closed Muse Spark frontier model and released under Apache 2.0. With 4-bit quantization it runs multi-step tool calling, multimodal reasoning, and failure recovery on a single 24GB consumer GPU without cloud dependencies. It leads its size class on agentic orchestration but trails Qwen3.6-27B on terminal and OS automation — reach for it if you need a capable local agent that stays offline, not if you need independent reproducibility or low-level OS control.

Input

Free (open weights)

Output

Free (open weights)

Context

131K tokens

Muse Spark 1.1

Meta

Conditional

Meta's first paid AI model and first salvo in the commercial coding market — $1.25/$4.25 per 1M tokens with 1M context, native subagent orchestration, MCP/tool support, and computer-use capability at roughly 4x under Anthropic/OpenAI pricing. Leads on tool-use benchmarks but trails Opus 4.8 on pure coding by 7 points on SWE-Bench Pro. Closed-weight, US-only public preview. Best for teams prioritizing tool-use orchestration at low cost.

Input

$1.25 / 1M tokens

Output

$4.25 / 1M tokens

Context

1M tokens

Muse Spark 1.2

Meta

Conditional

Meta's coding-focused model update, co-trained with the Muse Code harness — 82.9% on Terminal-Bench 2.1 and 59.3% on DeepSWE 1.1, second only to Claude Opus 5 on the former and trailing both Opus 5 and GPT-5.6 Terra on the latter. Standard pricing unchanged from 1.1 at $1.25 input / $4.25 output per 1M tokens; contributor tier drops to $0.10/$0.20 in exchange for training-data rights. All published benchmarks remain vendor-run with no independent reproduction. Best for cost-sensitive coding teams willing to test a model explicitly optimized for its own harness.

Input

$1.25 / 1M tokens

Output

$4.25 / 1M tokens

Context

1M tokens

Muse Spark 1.3

Meta

Conditional

Muse Spark 1.3 is Meta's strongest coding and long-context model yet, and an easy drop-in for anyone already on 1.2: the standard token price is unchanged while software-engineering and million-token retrieval results move sharply up. The deciding caveat is that the headline agent numbers come from a max reasoning configuration Meta has not released, while the shipping tier runs slower and costs more per task. Right for cost-sensitive coding teams inside Muse Code; wrong for anyone who needs independently reproduced benchmarks.

Input

$1.25 / 1M tokens

Output

$4.25 / 1M tokens

Context

1M ctx; contributor tier $0.10/$0.20

Muse Spark Open

Meta

Conditional

Muse Spark Open is Meta's open-weight release of Muse Spark, an 816B mixture-of-experts model with 42B active parameters and a 1M-token context, available as downloads and from seven hosted providers. Conditional: the Muse Model License has limited commercial terms we did not read, the Meta pages we read give no hosted price, and Meta says its full evaluation report is still to come. It suits teams that want to self-host or shop between hosts, not buyers who need audited terms or independent benchmarks.

Input

Free (open weights)

Output

Free (open weights)

Context

1M ctx; Muse Model License (limited commercial terms); no hosted price read

Muse Wonder

Meta

Conditional

Muse Wonder is Meta's most capable model on its API, but it is still a preview and we could not read a single benchmark score behind the launch claims. Its list price is 3.2x Muse Spark 1.3 on input and 4.7x on output, and Meta says the full safety report is still to come. It suits teams already on Muse Spark who want to test it on long-horizon agent work, not buyers who need independently checked results.

Input

$4.00 / 1M tokens

Output

$20.00 / 1M tokens

Context

1M ctx; preview; cached $0.20

Mythos 5 (Claude Mythos 5)

Anthropic

Conditional

The strongest cybersecurity model in the world (83.8% CyberGym), built on Fable 5's weights with safety classifiers lifted. Access was suspended after the Jun 12 export directive; partially restored Jun 26 to ~100 designated US critical-infrastructure organizations (Project Glasswing). Verdict stays CONDITIONAL: capability is elite, but access is gated by federal designation. Not a public API: usable only if your organization is on the Commerce Department list.

Input

$10 / 1M tokens

Output

$50 / 1M tokens

Context

1M tokens (restricted access)

Nemotron 3.5 Super VL is NVIDIA's open-weight 120B vision-language model (12B active) under the OpenMDW-1.1 licence, with a 1M-token context and text, image and video input. The deciding caveat is hardware and evidence: NVIDIA recommends 4x H100 or 2x GB200 for the BF16 build, and the card's scores are NVIDIA's own runs. Suits teams that can host a large model on their own GPUs and want open weights for document and visual work.

Input

Not stated

Output

Not stated

Context

Open weights under OpenMDW-1.1; 1M ctx; hosted NIM and OpenRouter prices not verified

Ornith-1.0

DeepReinforce

Conditional

A strong MIT-licensed open-source agentic coding model family that achieves frontier-competitive benchmarks — beating Claude Opus 4.7 on both Terminal-Bench 2.1 and SWE-Bench Verified — but requires self-hosting with significant GPU resources and comes from a new lab with limited track record.

Input

Free (MIT license)

Output

Free (MIT license)

Context

256K tokens

Palmyra X6

Writer

Conditional

Palmyra X6 is Writer's flagship agentic model for marketing and revenue workflows — post-trained on Z.ai's open-weight GLM-5.2 at $2/$8 per 1M tokens, a fraction of the frontier rate. Every headline number is Writer-run, and the GLM-5.2 provenance is a real procurement question. Adopt it inside Writer for cost-sensitive GTM agent work; look elsewhere for independently benchmarked general capability.

Input

$2 / 1M

Output

$8 / 1M

Context

1M context, 8K max output

Qwen-Image-2.1

Alibaba

Conditional

Qwen-Image-2.1 is Alibaba's open-weights model for both text-to-image generation and editing, with native transparent RGBA output and multi-image composition from up to 10 references. Conditional because the weights ship under the Qwen Research License, which allows non-commercial use only, and we found no readable benchmark scores. Right for researchers and hobbyists running image generation and editing locally through Diffusers.

Input

Free (non-commercial weights)

Output

Free (non-commercial weights)

Context

Qwen Research License; non-commercial

Qwen3.8-27B

Alibaba

Conditional

Qwen3.8-27B is Alibaba's 27-billion-parameter open-weight model, now shipped under Apache 2.0 with 262K native context and single-GPU deployment. It is the self-hostable companion to the 2.4T Qwen3.8-Max flagship. Benchmarks remain Alibaba-reported, so it lands at conditional: a real, runnable model whose vendor-claimed coding gains still await independent reproduction.

Input

Free (open weights — self-hosted)

Output

Free (open weights — self-hosted)

Context

262K native ctx, 1M extended; Apache 2.0

Qwen3.8-Omni-Flash

Alibaba

Conditional

Qwen3.8-Omni-Flash takes text, images, audio and video in one call with a 1M-token context, and Alibaba says it cut the per-hour price of audio input by more than 98% against the previous version. Conditional because every benchmark we found is Alibaba's own and the model answers in text only; spoken replies need a separate model, such as the Realtime variant, at higher rates. Right for teams building audio-visual agents on Alibaba Cloud.

Input

$0.15 / 1M

Output

$0.47 / 1M (text)

Context

1M ctx; cache hit $0.016/1M; International

Sakana Fugu

Sakana AI

Conditional

Multi-agent orchestrator that matches frontier models on key benchmarks by routing across a pool of expert agents — but opaque routing and platform dependency limit transparency.

Input

$5 / 1M tokens

Output

$30 / 1M tokens

Context

272K tokens (>272K: $10/$45 per 1M)

SWE-2

Cognition

Conditional

SWE-2 is Cognition's coding model for Devin, post-trained from Kimi K3, and Cognition claims it lands within one point of Fable 5.1 on FrontierCode 1.1 Main at 64% lower cost. Conditional because every SWE-2 score is vendor-reported, the same table shows it far behind Fable 5.1 and GPT-6 Astra on Terminal-Bench 4, and it runs only inside Devin. Right for Devin Desktop and CLI users; wrong for anyone who needs an API model or independently verified results.

Input

N/A (no standalone API)

Output

N/A (no standalone API)

Context

Devin only; Enterprise $0.75 in/$3.75 out per 1M

Ternary Bonsai 2 27B

PrismML

Conditional

Ternary Bonsai 2 27B squeezes Qwen3.8 27B into about 5.9GB at 1.76 effective bits, so a 262K-context text-and-image model fits on a Mac or a single consumer GPU under Apache 2.0. Conditional because the quality figures are PrismML's own, its blog and model card report different aggregate scores, and the files need PrismML's custom kernels: stock llama.cpp will not run them. Right for local-inference users short on memory.

Input

Free (open weights)

Output

Free (open weights)

Context

262K ctx; self-hosted; PrismML kernels required

Gemini 3.5 Flash Cyber

Google

Caution

Gemini 3.5 Flash Cyber is Google's first cybersecurity-specialized model, fine-tuned from 3.5 Flash to find, validate, and patch vulnerabilities. Inside CodeMender's multi-agent architecture, it reaches competitive performance on CyberGym against much larger models like Mythos at a fraction of the cost. But the restrictions are severe: it runs exclusively within CodeMender, available only to governments and trusted partners via a limited-access pilot with no public API, no published benchmark numbers, and no timeline for broader release. For the vast majority of developers, this model is simply inaccessible — watch for access expansion.

Input

N/A

Output

N/A

Context

CodeMender-only, government-gated pilot

Gemini 3.5 Pro

Google

pending

Google's pre-GA frontier model, now on its fourth postponement and 'months behind schedule' per Bloomberg. The 2M-token context, native multimodal pipeline, and Deep Think reasoning remain real capabilities, but no confirmed GA timeline exists. Coding capability is the core shortfall.

Input

Not yet published

Output

Not yet published

Context

Est. $5-15/$30-60 per 1M. 2M ctx. Not GA.

Gemini 4

Google

pending

Gemini 4 is Google DeepMind's confirmed next-generation frontier model — pre-training began July 21, 2026 on what Google calls its 'most ambitious' run yet. Sundar Pichai has positioned it as the answer to coding and agentic gaps, targeting where the frontier will be at launch, not where it sits today. But everything else is unknown: no benchmarks, no pricing, no specs, no GA release date, and the delayed Gemini 3.5 Pro's relationship to it remains unresolved. Watchlist-only until Google ships something concrete.

Input

$2 / 1M tokens (through Nov 30)

Output

$10 / 1M tokens (through Nov 30)

Context

$2/$10 to Nov 30, then $4/$20; paid tier only

Qwen3.8-Max

Alibaba

pending

Qwen3.8-Max is Alibaba's 2.4-trillion-parameter flagship, previewed July 2026 as a multimodal MoE model with a 984K context window. It is not yet GA: no per-token API pricing, no benchmark table, no open weights, and no model card have been published, and the "second only to Fable 5" claim is Alibaba's own unverified self-assessment. The preview is worth testing for coding and long-context agent workloads at $6/month entry cost, but wait for published benchmarks, per-token pricing, and the promised open-weight release before committing.

Input

$2.00 / 1M tokens

Output

$6.00 / 1M tokens

Context

Model Studio (Singapore); one tier up to 1M tokens