Devstral Small vs GPT-5.1 Codex mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Devstral Small 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, Devstral Small holds a notable pricing advantage for token ingestion. On output generation, Devstral Small 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.
원하는 AI 모델 선택 및 실시간 비교
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Devstral Small
오픈 웨이트 모델Mistral AI의 Devstral Small 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.15/1M 토큰, 출력 $0.45/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.15 / 1M
$0.45 / 1M
128K tokens
42%
GPT-5.1 Codex mini
심층 추론 모델OpenAI의 GPT-5.1 Codex mini 모델은 400K 토큰 컨텍스트 윈도우와 입력 $0.25/1M 토큰, 출력 $2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.25 / 1M
$2.00 / 1M
400K tokens
55.5%
한눈에 보는 핵심 차이점
- Devstral Small is 3.1x 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 95 tok/s.
- GPT-5.1 Codex mini leads on SWE-bench coding (55.5% vs 42.0%).
| Feature | Devstral Small | GPT-5.1 Codex mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 47 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.15 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $0.45 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 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 | 16K | 128K |
Throughput Speed Tokens generated per second | 95 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 55.5% |
GPQA Diamond Graduate-level reasoning | 50% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.03 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Devstral Small · Strengths & Key Advantages
- Mistral AI의 첨단 Code-Optimized Autoregressive Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.15/1M, 출력 $0.45/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
GPT-5.1 Codex mini · Strengths & Key Advantages
- OpenAI의 첨단 Compact code-specialised reasoning model 아키텍처와 최대 400K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.25/1M, 출력 $2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Devstral Small | GPT-5.1 Codex mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $0.33 |
Output Tokens / Task Tokens generated per task | 30K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 23K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Devstral Small | GPT-5.1 Codex mini |
|---|---|---|
| Official API Model ID (SDK) | devstral-small-latest | gpt-5-1-codex-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-07 | 2025-11 |
| Knowledge Cutoff | May 2025 | October 2024 |
| Architecture | Code-Optimized Autoregressive Transformer | Compact code-specialised reasoning model |
| 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 | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 128K window | Flat rate ($0.25/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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 GPT-5.1 Codex mini: 핵심 요약 문답 (FAQ)
Which is more intelligent, Devstral Small or GPT-5.1 Codex mini?
GPT-5.1 Codex mini is more intelligent. It scores 47 compared with 45 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 Codex mini is faster, generating 180 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
Devstral Small is cheaper at $0.11 per 1M tokens blended, compared with $0.27 (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 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.
종합 평가
Verdict & Model Selection Recommendation: Choose Devstral Small if your priority is Local IDE coding companions, automated unit test generation, and secure on-premise PR reviews. 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.
자주 묻는 질문 (FAQ)
How does Devstral Small compare to GPT-5.1 Codex mini in API token pricing and cost per token?+
Devstral Small costs $0.15 per 1M input tokens and $0.45 per 1M output tokens, whereas GPT-5.1 Codex mini costs $0.25 input and $2.00 output. Overall, Devstral Small 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 Small or GPT-5.1 Codex mini?+
Devstral Small 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 Small or GPT-5.1 Codex mini?+
Devstral Small scores 42% 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 Small and GPT-5.1 Codex mini?+
Devstral Small is optimized for low-latency direct generation. 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 Small and GPT-5.1 Codex mini?+
Devstral Small generates approximately 95 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 Small and GPT-5.1 Codex mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Devstral Small to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.1 Codex mini. 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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- Devstral Small vs Codestral: Benchmarks, Pricing & Speed Comparison비교
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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.
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