Devstral Small vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Devstral Small by Mistral AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Devstral Small
IA Agentique et Outils AutonomesDevstral Small de Mistral AI est un modèle d’IA conçu pour les flux de travail agentiques autonomes, l’appel d’outils et l’orchestration multi-étapes, offrant une fenêtre de contexte de 128K, une vitesse de 95 tokens/s, un indice d’intellig
$0.15 / 1M
$0.45 / 1M
128K tokens
42%
Claude Opus 5.5
Modèle Phare de PointeClaude Opus 5.5 de Anthropic est un modèle d’IA conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production IA, offrant une fenêtre de contexte de 1M, une vitesse de 78 tokens/s, un indice d’intelligence de 9
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Différences Clés en un Coup d’Œil
- Devstral Small is 34.9x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Devstral Small is faster at 95 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 42.0%).
| Caractéristique | Devstral Small | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 94 |
Provider | Mistral AI | Anthropic |
Input Price Cost per million input tokens | $0.15 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.45 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 95 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 84.6% |
GPQA Diamond Graduate-level reasoning | 50% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.40 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Devstral Small · Strengths & Key Advantages
- Fenêtre de contexte de 128K et capacité de sortie maximale de 16K tokens (SWE-bench Verified 42% · GPQA Diamond 50%)
- Débit rapide de 95 tokens/seconde avec une latence au premier token (TTFT) de 420 ms
- Poids ouverts (24B) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Claude Opus 5.5 · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- Débit rapide de 78 tokens/seconde avec une latence au premier token (TTFT) de 1400 ms
- Tarification API optimisée ($4/$20 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Devstral Small | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $3.85 |
Output Tokens / Task Tokens generated per task | 30K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Devstral Small | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | devstral-small-latest | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | June 2026 |
| Architecture | Code-Optimized Autoregressive Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 24B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Devstral Small vs Claude Opus 5.5: Réponses Rapides (FAQ)
Which is more intelligent, Devstral Small or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 45 on the composite Intelligence Index.
Which model is faster?
Devstral Small is faster, generating 95 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Devstral Small is cheaper at $0.11 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Devstral Small has lower latency, with a time to first token of 0.42s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose Devstral Small for conçu pour les flux de travail agentiques autonomes, l’appel d’outils et l’orchestration multi-étapes, l’intégration rag et les pipelines de production évolutifs., or choose Claude Opus 5.5 for conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production ia, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Devstral Small or Claude Opus 5.5?+
Devstral Small costs $0.15/1M input and $0.45/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Devstral Small or Claude Opus 5.5?+
Devstral Small supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Mistral AI & Anthropic Official Documentation