GPT-5.1 Codex vs Devstral 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.1 Codex by OpenAI and Devstral 2 by Mistral AI 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 holds a notable pricing advantage for token ingestion. On output generation, Devstral 2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.1 Codex provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.1 Codex 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.
بدّل وقارن بين أي نموذجين
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GPT-5.1 Codex
نموذج استدلال عميقنموذج GPT-5.1 Codex المقدم من OpenAI يوفر نافذة سياق تصل إلى 400K رمز، بتكلفة $1.25 لكل مليون رمز إدخال و$10 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$1.25 / 1M
$10.00 / 1M
400K tokens
76.1%
Devstral 2
نموذج مفتوح الأوزاننموذج Devstral 2 المقدم من Mistral AI يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $1.5 لكل مليون رمز إدخال و$4.5 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$1.50 / 1M
$4.50 / 1M
262.1K tokens
58.2%
أهم الفروقات في لمحة
- Devstral 2 is 1.6x cheaper for API usage (input + output tokens combined).
- GPT-5.1 Codex has a larger context window (400K vs 262K tokens).
- GPT-5.1 Codex is faster at 125 tok/s vs 70 tok/s.
- GPT-5.1 Codex leads on SWE-bench coding (76.1% vs 58.2%).
| Feature | GPT-5.1 Codex | Devstral 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 61 | 56 |
Provider | OpenAI | Mistral AI |
Input Price Cost per million input tokens | $1.25 / 1M | $1.50 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $4.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.34 / 1M | $0.99 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 128K | 66K |
Throughput Speed Tokens generated per second | 125 tok/s | 70 tok/s |
SWE-bench Verified Real-world software engineering | 76.1% | 58.2% |
GPQA Diamond Graduate-level reasoning | 78.5% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.13 / 1M | $0.35 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.1 Codex · Strengths & Key Advantages
- بنية Code-specialised unified reasoning model متطورة من OpenAI مع سعة سياق تبلغ 400K رمز
- كفاءة عالية في التكلفة ($1.25/1M إدخال و$10/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Devstral 2 · Strengths & Key Advantages
- بنية Advanced Agentic Code Transformer متطورة من Mistral AI مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($1.5/1M إدخال و$4.5/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.1 Codex | Devstral 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.65 | $0.65 |
Output Tokens / Task Tokens generated per task | 58K | 42K |
Reasoning Tokens / Task Thinking tokens burned per task | 33K | 20K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.1 Codex | Devstral 2 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-1-codex | devstral-2-latest |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-11 | 2025-12 |
| Knowledge Cutoff | October 2024 | October 2025 |
| Architecture | Code-specialised unified reasoning model | Advanced Agentic Code Transformer |
| Parameters | Confidential | 123B |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($1.25/1M in) across full 400K window | Flat rate ($1.5/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.125/1M (no extra cache-write fee) | Cached input at $0.35/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
GPT-5.1 Codex vs Devstral 2: إجابات سريعة (FAQ)
Which is more intelligent, GPT-5.1 Codex or Devstral 2?
GPT-5.1 Codex is more intelligent. It scores 61 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 Codex is faster, generating 125 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
Devstral 2 is cheaper at $0.99 per 1M tokens blended, compared with $1.34 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Devstral 2 has lower latency, with a time to first token of 0.52s compared with 1.20s.
Which model has a larger context window?
GPT-5.1 Codex has a larger context window, supporting 400K tokens compared with 262.1K tokens.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose GPT-5.1 Codex if your priority is Code-centric product teams running agentic development. Its performance in demanding tasks justifies its operational token cost. Choose Devstral 2 if your workflow prioritizes Whole-repository migrations, autonomous developer agents, and complex architecture refactors. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does GPT-5.1 Codex compare to Devstral 2 in API token pricing and cost per token?+
GPT-5.1 Codex costs $1.25 per 1M input tokens and $10.00 per 1M output tokens, whereas Devstral 2 costs $1.50 input and $4.50 output. Overall, GPT-5.1 Codex 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: GPT-5.1 Codex or Devstral 2?+
GPT-5.1 Codex supports a context window of 400K tokens (~300k words), while Devstral 2 supports 262K tokens (~197k words). Therefore, GPT-5.1 Codex can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.1 Codex or Devstral 2?+
GPT-5.1 Codex scores 76.1% on SWE-bench Verified while Devstral 2 scores 58.2%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.1 Codex and Devstral 2?+
GPT-5.1 Codex features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Devstral 2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.1 Codex and Devstral 2?+
GPT-5.1 Codex generates approximately 125 tokens per second, while Devstral 2 delivers 70 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.1 Codex and Devstral 2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Codex to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Devstral 2. 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.
Related reading
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs