GPT-5.1 Codex Max vs Devstral 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.1 Codex Max 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, Devstral 2 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 Max provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.1 Codex Max 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.
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GPT-5.1 Codex Max
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-5.1 Codex Max, 400K token bağlam penceresi ve 1M girdi başına $1.75, 1M çıktı başına $14 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.2%
Devstral 2
Açık Ağırlıklı ModelMistral AI tarafından geliştirilen Devstral 2, 262K token bağlam penceresi ve 1M girdi başına $1.5, 1M çıktı başına $4.5 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$1.50 / 1M
$4.50 / 1M
262.1K tokens
58.2%
Bir Bakışta Temel Farklar
- Devstral 2 is 2.3x cheaper for API usage (input + output tokens combined).
- GPT-5.1 Codex Max has a larger context window (400K vs 262K tokens).
- GPT-5.1 Codex Max is faster at 95 tok/s vs 70 tok/s.
- GPT-5.1 Codex Max leads on SWE-bench coding (78.2% vs 58.2%).
| Feature | GPT-5.1 Codex Max | Devstral 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 62 | 56 |
Provider | OpenAI | Mistral AI |
Input Price Cost per million input tokens | $1.75 / 1M | $1.50 / 1M |
Output Price Cost per million output tokens | $14.00 / 1M | $4.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.87 / 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 | 95 tok/s | 70 tok/s |
SWE-bench Verified Real-world software engineering | 78.2% | 58.2% |
GPQA Diamond Graduate-level reasoning | 80% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.17 / 1M | $0.35 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.1 Codex Max · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Maximum-capability code-specialised reasoning model mimarisi ve 400K token bağlam kapasitesi
- Dengeli API maliyeti ($1.75/1M girdi, $14/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Devstral 2 · Strengths & Key Advantages
- Mistral AI imzalı gelişmiş Advanced Agentic Code Transformer mimarisi ve 262K token bağlam kapasitesi
- Dengeli API maliyeti ($1.5/1M girdi, $4.5/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.1 Codex Max | Devstral 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.40 | $0.65 |
Output Tokens / Task Tokens generated per task | 62K | 42K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 20K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.1 Codex Max | Devstral 2 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-1-codex-max | 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 | Maximum-capability code-specialised 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 ($1.75/1M in) across full 400K window | Flat rate ($1.5/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.175/1M (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 Max vs Devstral 2: Hızlı Yanıtlar (SSS)
Which is more intelligent, GPT-5.1 Codex Max or Devstral 2?
GPT-5.1 Codex Max is more intelligent. It scores 62 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 Codex Max is faster, generating 95 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 $1.87 (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 1.70s.
Which model has a larger context window?
GPT-5.1 Codex Max has a larger context window, supporting 400K tokens compared with 262.1K tokens.
Sonuç
Verdict & Model Selection Recommendation: Choose GPT-5.1 Codex Max if your priority is Enterprise-scale code agents and monorepo work. 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.
Sıkça Sorulan Sorular (SSS)
How does GPT-5.1 Codex Max compare to Devstral 2 in API token pricing and cost per token?+
GPT-5.1 Codex Max costs $1.75 per 1M input tokens and $14.00 per 1M output tokens, whereas Devstral 2 costs $1.50 input and $4.50 output. Overall, Devstral 2 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 Max or Devstral 2?+
GPT-5.1 Codex Max supports a context window of 400K tokens (~300k words), while Devstral 2 supports 262K tokens (~197k words). Therefore, GPT-5.1 Codex Max 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 Max or Devstral 2?+
GPT-5.1 Codex Max scores 78.2% on SWE-bench Verified while Devstral 2 scores 58.2%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex Max holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.1 Codex Max and Devstral 2?+
GPT-5.1 Codex Max 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 Max and Devstral 2?+
GPT-5.1 Codex Max generates approximately 95 tokens per second, while Devstral 2 delivers 70 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex Max 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 Max and Devstral 2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Devstral 2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.1 Codex Max. 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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- GPT-5.2 Codex vs Devstral 2: Benchmarks, Pricing & Speed ComparisonKarşılaştır
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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs