Model Comparison
Qwen3.6-27B vs Mistral Medium 3.5Which is better in 2026?
Both models are evenly matched across the benchmarks. Qwen3.6-27B is 2.2x cheaper per token.
Verdict: Qwen3.6-27B vs Mistral Medium 3.5 — which is better?
Qwen3.6-27B (by Alibaba Cloud / Qwen Team) and Mistral Medium 3.5 (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Qwen3.6-27B outperforms in 1 benchmarks (GDPval-AA), while Mistral Medium 3.5 is better at 1 benchmark (SWE-Bench Verified). Both models are evenly matched across the benchmarks.
On price, Qwen3.6-27B is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.6-27B also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose Qwen3.6-27B if…
- cost matters — it's about 2.2x cheaper per token
- you process long inputs — it offers a 262,144 token context window
Choose Mistral Medium 3.5 if…
- you want the most recent training data — it shipped Apr 2026
Performance Benchmarks
Comparative analysis across standard metrics
Qwen3.6-27B outperforms in 1 benchmarks (GDPval-AA), while Mistral Medium 3.5 is better at 1 benchmark (SWE-Bench Verified).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Qwen3.6-27B ($0.60/1M tokens) is 2.5x cheaper than Mistral Medium 3.5 ($1.50/1M tokens).
For output processing, Qwen3.6-27B ($3.60/1M tokens) is 2.1x cheaper than Mistral Medium 3.5 ($7.50/1M tokens).
In conclusion, Mistral Medium 3.5 is more expensive than Qwen3.6-27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Medium 3.5 has 100.2B more parameters than Qwen3.6-27B, making it 360.7% larger.
Context Window
Maximum input and output token capacity
Qwen3.6-27B accepts 262,144 input tokens compared to Mistral Medium 3.5's 256,000 tokens. Mistral Medium 3.5 can generate longer responses up to 256,000 tokens, while Qwen3.6-27B is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Both Qwen3.6-27B and Mistral Medium 3.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen3.6-27B
Mistral Medium 3.5
License
Usage and distribution terms
Qwen3.6-27B is licensed under Apache 2.0, while Mistral Medium 3.5 uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
Qwen3.6-27B was released on 2026-04-21, while Mistral Medium 3.5 was released on 2026-04-29.
Mistral Medium 3.5 is 0 month newer than Qwen3.6-27B.
Apr 21, 2026
2 months ago
Apr 29, 2026
2 months ago
1w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Qwen3.6-27B is available from Novita. Mistral Medium 3.5 is available from Mistral AI.
Qwen3.6-27B
Mistral Medium 3.5
Outputs Comparison
Key Takeaways
Qwen3.6-27B
View detailsAlibaba Cloud / Qwen Team
Mistral Medium 3.5
View detailsMistral AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Qwen3.6-27B and Mistral Medium 3.5 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Qwen3.6-27B vs Mistral Medium 3.5.