| Signal | GPT-5.2-Codex | Delta | Mistral Large 3 2512 |
|---|---|---|---|
Capabilities | 100 | +33 | |
Pricing | 14 | +13 | |
Context window size | 89 | +3 | |
Recency | 100 | -- | |
Output Capacity | 85 | +65 | |
| Overall Result | 4 wins | of 5 | 0 wins |
30
days ranked higher
0
days
0
days ranked higher
OpenAI
Mistral AI
Mistral Large 3 2512 saves you $750.00/month
That's $9000.00/year compared to GPT-5.2-Codex at your current usage level of 100K calls/month.
| Metric | GPT-5.2-Codex | Mistral Large 3 2512 | Winner |
|---|---|---|---|
| Overall Score | 96 | 70 | GPT-5.2-Codex |
| Rank | #9 | #123 | GPT-5.2-Codex |
| Quality Rank | #9 | #123 | GPT-5.2-Codex |
| Adoption Rank | #9 | #123 | GPT-5.2-Codex |
| Parameters | -- | -- | -- |
| Context Window | 400K | 262K | GPT-5.2-Codex |
| Pricing | $1.75/$14.00/M | $0.50/$1.50/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 67 | GPT-5.2-Codex |
| Pricing | 14 | 2 | GPT-5.2-Codex |
| Context window size | 89 | 86 | GPT-5.2-Codex |
| Recency | 100 | 100 | GPT-5.2-Codex |
| Output Capacity | 85 | 20 | GPT-5.2-Codex |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 96/100 (rank #9), placing it in the top 97% of all 290 models tracked.
Scores 70/100 (rank #123), placing it in the top 58% of all 290 models tracked.
GPT-5.2-Codex has a 25-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Mistral Large 3 2512 offers 87% better value per quality point. At 1M tokens/day, you'd spend $30.00/month with Mistral Large 3 2512 vs $236.25/month with GPT-5.2-Codex — a $206.25 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Mistral Large 3 2512 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (400K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.50/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (96/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input — can analyze screenshots, diagrams, photos, and scanned documents directly
GPT-5.2-Codex clearly outperforms Mistral Large 3 2512 with a significant 25.299999999999997-point lead. For most general use cases, GPT-5.2-Codex is the stronger choice. However, Mistral Large 3 2512 may still excel in niche scenarios.
Best for Quality
GPT-5.2-Codex
Marginally better benchmark scores; both are excellent
Best for Cost
Mistral Large 3 2512
87% lower pricing; better value at scale
Best for Reliability
GPT-5.2-Codex
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.2-Codex
Stronger community support and better developer experience
Best for Production
GPT-5.2-Codex
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-5.2-Codex | Mistral Large 3 2512 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Mistral AI
Mistral Large 3 2512 saves you $17.25/month
That's 86% cheaper than GPT-5.2-Codex at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | GPT-5.2-Codex | Mistral Large 3 2512 |
|---|---|---|
| Context Window | 400K | 262K |
| Max Output Tokens | 128,000 | -- |
| Open Source | No | No |
| Created | Jan 14, 2026 | Dec 1, 2025 |
GPT-5.2-Codex scores 96/100 (rank #9) compared to Mistral Large 3 2512's 70/100 (rank #123), giving it a 25-point advantage. GPT-5.2-Codex is the stronger overall choice, though Mistral Large 3 2512 may excel in specific areas like cost efficiency.
GPT-5.2-Codex is ranked #9 and Mistral Large 3 2512 is ranked #123 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Mistral Large 3 2512 is cheaper at $1.50/M output tokens vs GPT-5.2-Codex's $14.00/M output tokens — 9.3x more expensive. Input token pricing: GPT-5.2-Codex at $1.75/M vs Mistral Large 3 2512 at $0.50/M.
GPT-5.2-Codex has a larger context window of 400,000 tokens compared to Mistral Large 3 2512's 262,144 tokens. A larger context window means the model can process longer documents and conversations.