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Discover how Mistral's Mistral Medium 3.5 and Alibaba's Qwen3.8-Max stack up against each other in this comprehensive comparison of two leading AI language models. Released in April 2026 and September 2026 respectively, these models represent significant advancements in artificial intelligence, with Mistral Medium 3.5 offering a 256,000-token context window and Qwen3.8-Max offering a 1,000,000-token context window.

Explore their capabilities, pricing, and performance metrics to find the right AI solution for your specific needs.

Models Overview

	Mistral Medium 3.5	Qwen3.8-Max
Provider The company that provides the model.	Mistral	Alibaba
Context Length Maximum number of tokens the model can process	256K	1M
Maximum Output Maximum number of tokens the model can generate in one response	Unknown	131.07K
Release Date When the model was first released.	28-04-2026	02-09-2026
Knowledge Cutoff When the model's training data ends.	Unknown	Unknown
Open Source Whether the model weights are openly available.	TRUE	TRUE

Pricing Comparison

Compare the pricing of Mistral's Mistral Medium 3.5 and Alibaba's Qwen3.8-Max to determine the most cost-effective solution for your AI needs. Prices are the standard API tier per million tokens, as published by each provider as of September 2026.

	Mistral Medium 3.5	Qwen3.8-Max
Input Cost Cost per million input tokens	$1.5 / 1M tokens	$2 / 1M tokens
Output Cost Cost per million tokens generated	$7.5 / 1M tokens	$6 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Mistral's Mistral Medium 3.5 and Alibaba's Qwen3.8-Max on industry benchmarks. Scores are the ones the providers and public leaderboards report; a benchmark neither reports is left out.

	Mistral Medium 3.5	Qwen3.8-Max
LMArena Elo Crowd-sourced blind preference rating on the LMArena text leaderboard.	Benchmark not available	1,481
GPQA Diamond Graduate-level science questions written to be search-proof.	Benchmark not available	92.6%
SWE-bench Pro Harder, contamination-resistant successor of SWE-bench Verified; not comparable with it.	Benchmark not available	67.7%