| Signal | Mistral Medium 3.1 | Delta | Qwen3 235B A22B Instruct 2507 |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 2 | +2 | |
Context window size | 81 | -5 | |
Recency | 94 | +4 | |
Output Capacity | 20 | -- | |
| Overall Result | 2 wins | of 5 | 1 wins |
9
days ranked higher
3
days
18
days ranked higher
Mistral AI
Alibaba
Qwen3 235B A22B Instruct 2507 saves you $127.90/month
That's $1534.80/year compared to Mistral Medium 3.1 at your current usage level of 100K calls/month.
| Metric | Mistral Medium 3.1 | Qwen3 235B A22B Instruct 2507 | Winner |
|---|---|---|---|
| Overall Score | 68 | 68 | Qwen3 235B A22B Instruct 2507 |
| Rank | #152 | #151 | Qwen3 235B A22B Instruct 2507 |
| Quality Rank | #152 | #151 | Qwen3 235B A22B Instruct 2507 |
| Adoption Rank | #152 | #151 | Qwen3 235B A22B Instruct 2507 |
| Parameters | -- | 235B | -- |
| Context Window | 131K | 262K | Qwen3 235B A22B Instruct 2507 |
| Pricing | $0.40/$2.00/M | $0.07/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Mistral Medium 3.1 |
| Pricing | 2 | 0 | Mistral Medium 3.1 |
| Context window size | 81 | 86 | Qwen3 235B A22B Instruct 2507 |
| Recency | 94 | 90 | Mistral Medium 3.1 |
| Output Capacity | 20 | 20 | Mistral Medium 3.1 |
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 68/100 (rank #152), placing it in the top 48% of all 290 models tracked.
Scores 68/100 (rank #151), placing it in the top 48% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal — your choice should depend on pricing, latency requirements, and specific feature needs.
Qwen3 235B A22B Instruct 2507 offers 93% better value per quality point. At 1M tokens/day, you'd spend $2.56/month with Qwen3 235B A22B Instruct 2507 vs $36.00/month with Mistral Medium 3.1 — a $33.44 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. Qwen3 235B A22B Instruct 2507 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (68/100) 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
Mistral Medium 3.1 and Qwen3 235B A22B Instruct 2507 are extremely close in overall performance (only 0.09999999999999432 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mistral Medium 3.1
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 235B A22B Instruct 2507
93% lower pricing; better value at scale
Best for Reliability
Mistral Medium 3.1
Higher uptime and faster response speeds
Best for Prototyping
Mistral Medium 3.1
Stronger community support and better developer experience
Best for Production
Mistral Medium 3.1
Wider enterprise adoption and proven at scale
by Mistral AI
by Alibaba
| Capability | Mistral Medium 3.1 | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Qwen3 235B A22B Instruct 2507 saves you $2.87/month
That's 92% cheaper than Mistral Medium 3.1 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 | Mistral Medium 3.1 | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Context Window | 131K | 262K |
| Max Output Tokens | -- | -- |
| Open Source | No | Yes |
| Created | Aug 13, 2025 | Jul 21, 2025 |
Qwen3 235B A22B Instruct 2507 scores 68/100 (rank #151) compared to Mistral Medium 3.1's 68/100 (rank #152), giving it a 0-point advantage. Qwen3 235B A22B Instruct 2507 is the stronger overall choice, though Mistral Medium 3.1 may excel in specific areas like certain benchmarks.
Mistral Medium 3.1 is ranked #152 and Qwen3 235B A22B Instruct 2507 is ranked #151 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.
Qwen3 235B A22B Instruct 2507 is cheaper at $0.10/M output tokens vs Mistral Medium 3.1's $2.00/M output tokens — 20.0x more expensive. Input token pricing: Mistral Medium 3.1 at $0.40/M vs Qwen3 235B A22B Instruct 2507 at $0.07/M.
Qwen3 235B A22B Instruct 2507 has a larger context window of 262,144 tokens compared to Mistral Medium 3.1's 131,072 tokens. A larger context window means the model can process longer documents and conversations.