Devstral Small vs GPT-5.1 Codex mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Devstral Small dari Mistral AI dan GPT-5.1 Codex mini dari OpenAI adalah dua model AI unggulan. Devstral Small menawarkan jendela konteks 128K token dengan harga $0.15/$0.45 per 1 juta token, sedangkan GPT-5.1 Codex mini menyediakan konteks 400K token seharga $0.25/$2.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Devstral Small
Model Agen Otonom & AlatDevstral Small dari Mistral AI adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 128K, kecepatan 95 token/detik, indeks kecerdasan 45/10
$0.15 / 1M
$0.45 / 1M
128K tokens
42%
GPT-5.1 Codex mini
Model Ringan & Hemat BiayaGPT-5.1 Codex mini dari OpenAI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 400K, kecepatan 180 token/detik, indeks kecerdasan 47/100, serta ta
$0.25 / 1M
$2.00 / 1M
400K tokens
55.5%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Devstral Small · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 42% · GPQA Diamond 50%)
- Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 420 ms
- Bobot terbuka (24B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT-5.1 Codex mini · Strengths & Key Advantages
- Jendela konteks 400K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 55.5% · GPQA Diamond 66.5%)
- Kecepatan generasi tinggi 180 token/detik dengan latensi token pertama (TTFT) hanya 720 ms
- Skema harga API kompetitif (tarif $0.25/$2 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Devstral Small (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.1 Codex mini (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-5.1 Codex mini unggul dalam penalaran dan pemrograman, GPT-5.1 Codex mini memberikan kecepatan generasi token lebih tinggi, dan Devstral Small lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Devstral Small atau GPT-5.1 Codex mini?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-5.1 Codex mini memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Devstral Small vs GPT-5.1 Codex mini?+
Devstral Small mengenakan biaya $0.15 / $0.45 per 1M token input/output, sedangkan GPT-5.1 Codex mini sebesar $0.25 / $2.00. Secara keseluruhan, Devstral Small lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Devstral Small mendukung konteks 128K token dibandingkan 400K token pada GPT-5.1 Codex mini, sementara GPT-5.1 Codex mini unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Devstral Medium vs GPT-5.1 Codex mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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- GPT-5.1 Codex mini vs GPT-5-Codex: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GPT-5.1 Codex vs GPT-5.1 Codex mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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- Aion-3.0 vs Aion-Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Mistral AI & OpenAI serta evaluasi benchmark independen (2026).