Devstral Medium vs GPT-5.1 Codex mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Devstral Medium by Mistral AI and GPT-5.1 Codex mini by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-5.1 Codex mini holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 Codex mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.1 Codex mini provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.1 Codex mini leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Changez et Comparez les Modèles
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Devstral Medium
Agent de codecommercial phare ingénierie logicielle agent fine-tuned pour terminal exécution, test-driven debugging, et end-à-end dépôt fixes.
$0.80 / 1M
$2.40 / 1M
128K tokens
51.5%
GPT-5.1 Codex mini
Codex compactun cheap codage-optimised Codex lane pour rapide edits et revue à échelle.
$0.25 / 1M
$2.00 / 1M
400K tokens
55.5%
Différences Clés en un Coup d’Œil
- GPT-5.1 Codex mini is 1.7x cheaper for API usage (input + output tokens combined).
- GPT-5.1 Codex mini has a larger context window (400K vs 128K tokens).
- GPT-5.1 Codex mini is faster at 180 tok/s vs 75 tok/s.
- GPT-5.1 Codex mini leads on SWE-bench coding (55.5% vs 51.5%).
| Caractéristique | Devstral Medium | GPT-5.1 Codex mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 47 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.80 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.54 / 1M | $0.27 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 51.5% | 55.5% |
GPQA Diamond Graduate-level reasoning | 62% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.03 / 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 Medium · Strengths & Key Advantages
- spécialement conçu autonome agent loop intégration avec bash et file outils
- Impressive 51.5% SWE-bench Verified score avec faible token burn
- supérieur multifichier consistency et unit test vérification
Considerations & Trade-offs
- propriétaire fermé-weights API seulement
- supérieur coût que ouvert Devstral petit
GPT-5.1 Codex mini · Strengths & Key Advantages
- Cheap, rapide codage assistance
- bon pour lint fixes, refactors et revue comments
- Retains le 400K fenêtre de contexte
- excellent pour linting, formatage et petit refactors
Considerations & Trade-offs
- Struggles sur grand, interdependent repositories
- moins fiable que complet Codex sur multifichier refactors
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Devstral Medium | GPT-5.1 Codex mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.55 | $0.33 |
Output Tokens / Task Tokens generated per task | 38K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 23K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Devstral Medium | GPT-5.1 Codex mini |
|---|---|---|
| Official API Model ID (SDK) | devstral-medium-latest | gpt-5-1-codex-mini |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-07 | 2025-11 |
| Knowledge Cutoff | May 2025 | October 2024 |
| Architecture | High-Capacity Agentic Code Transformer | Compact code-specialised reasoning model |
| Parameters | Confidential (~70B) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.8/1M in) across full 128K window | Flat rate ($0.25/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Cached input at $0.2/1M tokens | Automatic prefix caching at $0.025/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Mistral AI Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Devstral Medium vs GPT-5.1 Codex mini: Réponses Rapides (FAQ)
Which is more intelligent, Devstral Medium or GPT-5.1 Codex mini?
Devstral Medium is more intelligent. It scores 52 compared with 47 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 Codex mini is faster, generating 180 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
GPT-5.1 Codex mini is cheaper at $0.27 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Devstral Medium has lower latency, with a time to first token of 0.50s compared with 0.72s.
Which model has a larger context window?
GPT-5.1 Codex mini has a larger context window, supporting 400K tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Devstral Medium if your priority is Autonomous bug-fixing bots, CI/CD pipeline automation, and enterprise repository refactoring. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.1 Codex mini if your workflow prioritizes High-volume code automation and inline review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Devstral Medium compare to GPT-5.1 Codex mini in API token pricing and cost per token?+
Devstral Medium costs $0.80 per 1M input tokens and $2.40 per 1M output tokens, whereas GPT-5.1 Codex mini costs $0.25 input and $2.00 output. Overall, GPT-5.1 Codex mini is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Devstral Medium or GPT-5.1 Codex mini?+
Devstral Medium supports a context window of 128K tokens (~96k words), while GPT-5.1 Codex mini supports 400K tokens (~300k words). Therefore, GPT-5.1 Codex mini can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Devstral Medium or GPT-5.1 Codex mini?+
Devstral Medium scores 51.5% on SWE-bench Verified while GPT-5.1 Codex mini scores 55.5%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Devstral Medium and GPT-5.1 Codex mini?+
Devstral Medium features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.1 Codex mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Devstral Medium and GPT-5.1 Codex mini?+
Devstral Medium generates approximately 75 tokens per second, while GPT-5.1 Codex mini delivers 180 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Devstral Medium and GPT-5.1 Codex mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Codex mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Devstral Medium. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs