| Signal | Pixtral Large 2411 | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 67 | -17 | |
Pricing | 6 | +6 | |
Context window size | 81 | -14 | |
Recency | 44 | -56 | |
Output Capacity | 20 | -60 | |
Benchmarks | 0 | -67 | |
| Overall Result | 1 wins | of 6 | 5 wins |
0
days ranked higher
0
days
30
days ranked higher
Mistral AI
Alibaba
Qwen3.5-Flash saves you $480.50/month
That's $5766.00/year compared to Pixtral Large 2411 at your current usage level of 100K calls/month.
| Metric | Pixtral Large 2411 | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 56 | 79 | Qwen3.5-Flash |
| Rank | #244 | #80 | Qwen3.5-Flash |
| Quality Rank | #244 | #80 | Qwen3.5-Flash |
| Adoption Rank | #244 | #80 | Qwen3.5-Flash |
| Parameters | -- | -- | -- |
| Context Window | 131K | 1000K | Qwen3.5-Flash |
| Pricing | $2.00/$6.00/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-Flash |
| Pricing | 6 | 0 | Pixtral Large 2411 |
| Context window size | 81 | 95 | Qwen3.5-Flash |
| Recency | 44 | 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 56/100 (rank #244), placing it in the top 16% 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 24-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 96% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $120.00/month with Pixtral Large 2411 - a $115.13 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-Flash 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 ($0.26/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 Pixtral Large 2411 with a significant 23.700000000000003-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, Pixtral Large 2411 may still excel in niche scenarios.
Best for Quality
Pixtral Large 2411
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
96% lower pricing; better value at scale
Best for Reliability
Pixtral Large 2411
Higher uptime and faster response speeds
Best for Prototyping
Pixtral Large 2411
Stronger community support and better developer experience
Best for Production
Pixtral Large 2411
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Pixtral Large 2411 | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Qwen3.5-Flash saves you $10.37/month
That's 96% cheaper than Pixtral Large 2411 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 | Pixtral Large 2411 | Qwen3.5-Flash |
|---|---|---|
| Context Window | 131K | 1M |
| Max Output Tokens | -- | 65,536 |
| Open Source | No | No |
| Created | Nov 19, 2024 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Pixtral Large 2411's 56/100 (rank #244), giving it a 24-point advantage. Qwen3.5-Flash is the stronger overall choice, though Pixtral Large 2411 may excel in specific areas like certain benchmarks.
Pixtral Large 2411 is ranked #244 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.
Qwen3.5-Flash is cheaper at $0.26/M output tokens vs Pixtral Large 2411's $6.00/M output tokens - 23.1x more expensive. Input token pricing: Pixtral Large 2411 at $2.00/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Pixtral Large 2411's 131,072 tokens. A larger context window means the model can process longer documents and conversations.