| Signal | Mistral Large | Delta | Qwen3.5-9B |
|---|---|---|---|
Capabilities | 50 | -33 | |
Benchmarks | 68 | +2 | |
Pricing | 94 | -6 | |
Context window size | 81 | -5 | |
Recency | 0 | -100 | |
Output Capacity | 20 | -55 | |
| Overall Result | 1 wins | of 6 | 5 wins |
Score History
65.9
current score
Qwen3.5-9B
right now
66.5
current score
Mistral AI
Alibaba
Qwen3.5-9B saves you $487.50/month
That's $5850.00/year compared to Mistral Large at your current usage level of 100K calls/month.
| Metric | Mistral Large | Qwen3.5-9B | Winner |
|---|---|---|---|
| Overall Score | 66 | 67 | Qwen3.5-9B |
| Rank | #114 | #113 | Qwen3.5-9B |
| Quality Rank | #114 | #113 | Qwen3.5-9B |
| Adoption Rank | #114 | #113 | Qwen3.5-9B |
| Parameters | -- | 9B | -- |
| Context Window | 128K | 256K | Qwen3.5-9B |
| Pricing | $2.00/$6.00/M | $0.05/$0.15/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 83 | Qwen3.5-9B |
| Benchmarks | 68 | 66 | Mistral Large |
| Pricing | 94 | 100 | Qwen3.5-9B |
| Context window size | 81 | 86 | Qwen3.5-9B |
| Recency | 0 | 100 | Qwen3.5-9B |
| Output Capacity | 20 | 75 | Qwen3.5-9B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 66/100 (rank #114), placing it in the top 61% of all 290 models tracked.
Scores 67/100 (rank #113), placing it in the top 61% of all 290 models tracked.
With only a 1-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.
Qwen3.5-9B offers 98% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Qwen3.5-9B vs $120.00/month with Mistral Large - a $117.00 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. Qwen3.5-9B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (256K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.15/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (67/100) 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 Large and Qwen3.5-9B are extremely close in overall performance (only 0.5999999999999943 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mistral Large
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-9B
98% lower pricing; better value at scale
Best for Reliability
Mistral Large
Higher uptime and faster response speeds
Best for Prototyping
Mistral Large
Stronger community support and better developer experience
Best for Production
Mistral Large
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Mistral Large | Qwen3.5-9B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Qwen3.5-9B saves you $10.53/month
That's 98% cheaper than Mistral Large 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 Large | Qwen3.5-9B |
|---|---|---|
| Context Window | 128K | 256K |
| Max Output Tokens | -- | 32,768 |
| Open Source | No | Yes |
| Created | Feb 26, 2024 | Mar 10, 2026 |