| Signal | Mistral Small 3.2 24B | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 67 | -17 | |
Pricing | 0 | 0 | |
Context window size | 81 | -14 | |
Recency | 83 | -17 | |
Output Capacity | 20 | -60 | |
Benchmarks | 0 | -67 | |
| Overall Result | 0 wins | of 6 | 6 wins |
0
days ranked higher
0
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30
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Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $2.00/month
That's $24.00/year compared to Qwen3.5-Flash at your current usage level of 100K calls/month.
| Metric | Mistral Small 3.2 24B | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 67 | 79 | Qwen3.5-Flash |
| Rank | #174 | #80 | Qwen3.5-Flash |
| Quality Rank | #174 | #80 | Qwen3.5-Flash |
| Adoption Rank | #174 | #80 | Qwen3.5-Flash |
| Parameters | 24B | -- | -- |
| Context Window | 128K | 1000K | Qwen3.5-Flash |
| Pricing | $0.07/$0.20/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-Flash |
| Pricing | 0 | 0 | Qwen3.5-Flash |
| Context window size | 81 | 95 | Qwen3.5-Flash |
| Recency | 83 | 100 | Qwen3.5-Flash |
| Output Capacity | 20 | 80 | Qwen3.5-Flash |
| Benchmarks | -- | 67 | Qwen3.5-Flash |
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 67/100 (rank #174), placing it in the top 40% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
Qwen3.5-Flash has a 12-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Mistral Small 3.2 24B offers 15% better value per quality point. At 1M tokens/day, you'd spend $4.13/month with Mistral Small 3.2 24B vs $4.88/month with Qwen3.5-Flash - a $0.75 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 Small 3.2 24B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) 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
Qwen3.5-Flash clearly outperforms Mistral Small 3.2 24B with a significant 12-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, Mistral Small 3.2 24B may still excel in niche scenarios.
Best for Quality
Mistral Small 3.2 24B
Marginally better benchmark scores; both are excellent
Best for Cost
Mistral Small 3.2 24B
15% lower pricing; better value at scale
Best for Reliability
Mistral Small 3.2 24B
Higher uptime and faster response speeds
Best for Prototyping
Mistral Small 3.2 24B
Stronger community support and better developer experience
Best for Production
Mistral Small 3.2 24B
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Mistral Small 3.2 24B | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Mistral Small 3.2 24B saves you $0.0540/month
That's 13% cheaper than Qwen3.5-Flash 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 Small 3.2 24B | Qwen3.5-Flash |
|---|---|---|
| Context Window | 128K | 1M |
| Max Output Tokens | -- | 65,536 |
| Open Source | Yes | No |
| Created | Jun 20, 2025 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Mistral Small 3.2 24B's 67/100 (rank #174), giving it a 12-point advantage. Qwen3.5-Flash is the stronger overall choice, though Mistral Small 3.2 24B may excel in specific areas like cost efficiency.
Mistral Small 3.2 24B is ranked #174 and Qwen3.5-Flash is ranked #80 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 Small 3.2 24B is cheaper at $0.20/M output tokens vs Qwen3.5-Flash's $0.26/M output tokens - 1.3x more expensive. Input token pricing: Mistral Small 3.2 24B at $0.07/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Mistral Small 3.2 24B's 128,000 tokens. A larger context window means the model can process longer documents and conversations.