DeepSeek V4.1 Flash
DeepSeek · Sorti sept. 2026
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.
Est-ce fait pour vous ?
Recommandé pour
- High-volume API workloads at $0.15 input and $0.60 output per 1M tokens off-peak, with off-peak cache-hit input at $0.003 per 1M
- Scheduling around peak pricing: peak rates, twice off-peak, apply only 01:00-04:00 and 06:00-10:00 UTC Monday through Friday, and all other hours bill off-peak
- Self-hosting under the MIT licence, with the weights published on Hugging Face
- Terminal-style coding agents: DeepSeek's own max-effort table reports 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, the top score on both rows, ahead of Opus-5.0's 89.1 and 74.0
- Document and image understanding: Artificial Analysis lists text and image input, and DeepSeek's base-model table reports 95.6 on DocVQA
- Long-context work within a 1M-token window and up to 384K output tokens
Déconseillé pour
- Terminal-Bench 3.0 and 4.0: DeepSeek's own table reports 30.0 and 31.2, against Opus-5.0's 43.3 and 51.8
- Hard expert-knowledge questions: DeepSeek's table reports 36.8 on HLE, against Opus-5.0's 56.3 and GPT-5.6 Sol's 44.5
- Tight output-token budgets: Artificial Analysis measured 250M output tokens to run its Intelligence Index, against a 140M median for open-weight models of similar size
- Assuming the headline agent score carries over to your harness: DeepSeek's own scaffold table ranges from 65.5 under OpenCode to 74.2 under mini-SWE on DeepSWE v1.1
- Broad factual recall: DeepSeek's base-model table reports 42.3 on SimpleQA-Verified, against 55.2 for V4-Pro-Base
- Image generation: Artificial Analysis lists text as its only output modality
Performances par tâche
Terminal-style agentic coding
Tops DeepSeek's own max-effort table on Terminal-Bench 2.1 (90.6) and DeepSWE v1.1 (74.2). Vendor-run figures.
Terminal-Bench 3.0 and 4.0
31.2 on Terminal-Bench 4.0 in DeepSeek's table, against 51.8 for Opus-5.0.
Cost-efficient high-volume inference
$0.15/$0.60 per 1M off-peak, but Artificial Analysis records 250M output tokens on its index run against a 140M similar-size median.
Visual document understanding
95.6 on DocVQA in DeepSeek's base-model table; image input, text output. No independent vision measurement found.
Expert-level reasoning (HLE)
36.8 on HLE, against Opus-5.0's 56.3 in DeepSeek's table.
Tarifs
Entrée
$0.15 / 1M tokens
Sortie
$0.60 / 1M tokens
Contexte
1M ctx. Peak 2x: 01-04+06-10 UTC Mon-Fri
Tests de performance
| Test | Score | Source |
|---|---|---|
| Artificial Analysis Intelligence Index | 40 | Source |
| Terminal-Bench 2.1 (DeepSeek-run) | 90.6 | Source |
| Terminal-Bench 4.0 (DeepSeek-run) | 31.2 | Source |
| DeepSWE v1.1 (DeepSeek-run) | 74.2 | Source |
| GPQA Diamond (DeepSeek-run) | 90.9 | Source |
| HLE (DeepSeek-run) | 36.8 | Source |
| CyberGym (DeepSeek-run) | 88.1 | Source |
| Codeforces rating (DeepSeek-run) | 3471 | Source |
Aucun changement de verdict pour l’instant
L’horloge tourne dès le premier jour : les changements apparaîtront ici à mesure que notre verdict évolue.
Sources
Journal de vérification
Aucune vérification pour le moment
Nous n’avons pas encore enregistré de vérification pour cette entrée. Dès qu’une vérification s’exécute, son historique apparaît ici.