Pixtral Large vs Qwen3.8 Flash
Qwen3.8 Flash leads the LLM Stats Score 49.6 to 11.9. Qwen3.8 Flash is 13.0x cheaper per token.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Flash leads the overall LLM Stats Score 49.6 to 11.9, ranking #16 overall.
On price, Qwen3.8 Flash is roughly 13.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.8 Flash also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Pixtral Large
- you need open weights you can self-host or fine-tune
Choose Qwen3.8 Flash
- overall performance matters — it scores 49.6 and ranks #16 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 13.0x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for Pixtral Large · 22 for Qwen3.8 Flash
Pixtral Large and Qwen3.8 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Pixtral Large ($2.00/1M tokens) is 13.3x more expensive than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, Pixtral Large ($6.00/1M tokens) is 12.8x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, Pixtral Large is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Flash has 1.0B more parameters than Pixtral Large, making it 0.8% larger.
Context Window
Maximum input and output token capacity
Qwen3.8 Flash accepts 1,000,000 input tokens compared to Pixtral Large's 128,000 tokens. Qwen3.8 Flash can generate longer responses up to 131,072 tokens, while Pixtral Large is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Pixtral Large and Qwen3.8 Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Pixtral Large
Qwen3.8 Flash
License
Usage and distribution terms
Pixtral Large is licensed under Mistral Research License (MRL) for research; Mistral Commercial License for commercial use, while Qwen3.8 Flash uses a proprietary license.
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
Proprietary
Closed source
Release Timeline
When each model was launched
Pixtral Large was released on 2024-11-18, while Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 22 months newer than Pixtral Large.
Nov 18, 2024
1.8 years ago
Aug 26, 2026
4 days ago
1.8yr 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. Qwen3.8 Flash is available from Novita.
Pixtral Large
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Pixtral Large and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Pixtral Large vs Qwen3.8 Flash.