GPT-5.2 Codex vs Devstral 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.2 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, 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-5.2 Codex provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.2 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.
Cambia e Confronta Qualsiasi Modello
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GPT-5.2 Codex
Modello di Ragionamento ProfondoGPT-5.2 Codex di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 400K token e tariffazione API di $1.75/1M token in input e $14/1M in output, ideale per coding, ragionamento e automazione aziendale.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.1%
Devstral 2
Modello a Pesi ApertiDevstral 2 di Mistral AI è un modello di intelligenza artificiale con finestra di contesto da 262K token e tariffazione API di $1.5/1M token in input e $4.5/1M in output, ideale per coding, ragionamento e automazione aziendale.
$1.50 / 1M
$4.50 / 1M
262.1K tokens
58.2%
Differenze Chiave a Colpo d’Occhio
- Devstral 2 is 2.3x cheaper for API usage (input + output tokens combined).
- GPT-5.2 Codex has a larger context window (400K vs 262K tokens).
- GPT-5.2 Codex is faster at 118 tok/s vs 70 tok/s.
- GPT-5.2 Codex leads on SWE-bench coding (78.1% vs 58.2%).
| Feature | GPT-5.2 Codex | Devstral 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 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 | 118 tok/s | 70 tok/s |
SWE-bench Verified Real-world software engineering | 78.1% | 58.2% |
GPQA Diamond Graduate-level reasoning | 78.2% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.17 / 1M | $0.35 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.2 Codex · Strengths & Key Advantages
- Architettura Code-specialised long-horizon reasoning model avanzata di OpenAI con supporto fino a 400K token di contesto
- Ottimo rapporto qualità-prezzo ($1.75/1M input, $14/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Devstral 2 · Strengths & Key Advantages
- Architettura Advanced Agentic Code Transformer avanzata di Mistral AI con supporto fino a 262K token di contesto
- Ottimo rapporto qualità-prezzo ($1.5/1M input, $4.5/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.2 Codex | Devstral 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $0.65 |
Output Tokens / Task Tokens generated per task | 60K | 42K |
Reasoning Tokens / Task Thinking tokens burned per task | 34K | 20K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.2 Codex | Devstral 2 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-2-codex | devstral-2-latest |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2025-12 |
| Knowledge Cutoff | August 2025 | October 2025 |
| Architecture | Code-specialised long-horizon 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-5.2 Codex vs Devstral 2: Risposte Rapide (FAQ)
Which is more intelligent, GPT-5.2 Codex or Devstral 2?
GPT-5.2 Codex is more intelligent. It scores 66 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-5.2 Codex is faster, generating 118 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 1.28s.
Which model has a larger context window?
GPT-5.2 Codex has a larger context window, supporting 400K tokens compared with 262.1K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose GPT-5.2 Codex if your priority is Autonomous software agents and large migrations. 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.
Domande Frequenti (FAQ)
How does GPT-5.2 Codex compare to Devstral 2 in API token pricing and cost per token?+
GPT-5.2 Codex 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-5.2 Codex or Devstral 2?+
GPT-5.2 Codex supports a context window of 400K tokens (~300k words), while Devstral 2 supports 262K tokens (~197k words). Therefore, GPT-5.2 Codex can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.2 Codex or Devstral 2?+
GPT-5.2 Codex scores 78.1% on SWE-bench Verified while Devstral 2 scores 58.2%. For autonomous software engineering and multi-file debugging, GPT-5.2 Codex holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.2 Codex and Devstral 2?+
GPT-5.2 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-5.2 Codex and Devstral 2?+
GPT-5.2 Codex generates approximately 118 tokens per second, while Devstral 2 delivers 70 tokens per second. In latency-sensitive production environments, GPT-5.2 Codex provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.2 Codex 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-5.2 Codex. 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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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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs