Pixtral Large vs Qwen3-235B-A22B-Instruct-2507
Comparing Pixtral Large and Qwen3-235B-A22B-Instruct-2507 across benchmarks, pricing, and capabilities.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Pixtral Large and Qwen3-235B-A22B-Instruct-2507 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 9.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Instruct-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose Pixtral Large
- you want predictable pricing at $2.00/M input and $6.00/M output
Choose Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 9.6x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jul 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
Pixtral Large and Qwen3-235B-A22B-Instruct-2507don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Pixtral Large ($2.00/1M tokens) is 13.3x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).
For output processing, Pixtral Large ($6.00/1M tokens) is 7.5x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).
In conclusion, Pixtral Large is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-235B-A22B-Instruct-2507 has 111.0B more parameters than Pixtral Large, making it 89.5% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to Pixtral Large's 128,000 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 131,072 tokens, while Pixtral Large is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Pixtral Large supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
Pixtral Large
Qwen3-235B-A22B-Instruct-2507
License
Usage and distribution terms
Pixtral Large is licensed under Mistral Research License (MRL) for research; Mistral Commercial License for commercial use, while Qwen3-235B-A22B-Instruct-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Pixtral Large was released on 2024-11-18, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Qwen3-235B-A22B-Instruct-2507 is 8 months newer than Pixtral Large.
Nov 18, 2024
1.8 years ago
Jul 22, 2025
1.1 years ago
8mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Pixtral Large is available from Mistral AI. Qwen3-235B-A22B-Instruct-2507 is available from Fireworks, Novita.
Pixtral Large
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Pixtral Large and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Pixtral Large vs Qwen3-235B-A22B-Instruct-2507.