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Mistral Large 4.0 vs Qwen3.8 Max

Compare Mistral Large 4.0 from Mistral and Qwen3.8 Max from Qwen on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.

MistralMistral Large 4.0QwenQwen3.8 Max
Mistral logo
Mistral Large 4.0
Mistral · text, image → text

Mistral Large 4 is Mistral AI’s frontier multimodal model with text and image input, designed for complex reasoning, coding, and agentic workflows. It supports tool calling and structured outputs, with a 512K-token context window and up to 256K output tokens, making it well suited for long-context and multi-step tasks. Compared with earlier Mistral large models, Mistral Large 4 places greater emphasis on multimodal understanding, extended context, and agentic execution.

Input$1.36 /M
Output$4.18 /M
Cache read$0.1399 /M
Qwen logo
Qwen3.8 Max
Qwen · text, image, video → text

Qwen3.8-Max is Alibaba Cloud Tongyi Qianwen's next-generation flagship large language model, featuring a mixture-of-experts (MoE) architecture with 2.4 trillion parameters. It achieves another breakthrough in encoding depth, enabling it to handle more complex engineering-grade projects and long-term autonomous development; collaborative agent capabilities are significantly enhanced, performing more confidently in multi-tool orchestration and end-to-end delivery; visual understanding is comprehensively improved, with more sensitive and accurate chart reasoning, document parsing, and multimodal perception. Continuing the 1-million-context window, reasoning modes, and a complete tool ecosystem, it continues to evolve at a higher level of intelligence.

Input$1.69 /M
Output$5.07 /M
Cache read$0.169 /M

Pricing & Specifications

Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.

Mistral Large 4.0
Qwen3.8 Max
Input /M
$1.36
$1.69
Output /M
$4.18
$5.07
Cache read /M
$0.1399
$0.169
Context length
1,000,000
1,000,000
Max output
0
131,072
Time to First Token
-
12.5 s
Throughput
-
28.4 tok/s
Modalities
textimage
textimagevideo
Supported Parameters
toolsfunction callingstructured outputsweblong contextthinking
API Formats
Released
October 6, 2026
August 3, 2026

Promotional prices show the discounted rate; see each model page for promotion windows.

Activity Past 30 Days

Daily traffic served through AIHubMix — how demand for each model is trending.

mistral-large-4-0qwen3.8-max

Tokens / day

-

Requests / day

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Performance Past 3 Days

Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.

mistral-large-4-0qwen3.8-max

Throughput (tok/s)

-

TTFT (s)

-

Uptime (%)

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LMArena Benchmarks

LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.

Text
mistral-large-4-0qwen3.8-max
140014401480
Overall
14321482
WebDev Arena
mistral-large-4-0qwen3.8-max
1520158016401700
Overall
15401672

Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.

Cost calculator

Estimate your monthly bill for the same workload on each model.

Mistral Large 4.0
$144 /mo
Qwen3.8 Max
$177 /mo

Monthly = daily × 30. Discounted rates applied where a promotion is active.

FAQ

Which is cheaper: Mistral Large 4.0, Qwen3.8 Max?

Mistral Large 4.0: $4.18/M output tokens; Qwen3.8 Max: $5.07/M. Use the cost calculator above to estimate your own workload.

How large is each context window?

Mistral Large 4.0 accepts 1,000,000 and Qwen3.8 Max accepts 1,000,000 input tokens.

What inputs and capabilities does each model support?

Mistral Large 4.0 accepts text and image input; Qwen3.8 Max accepts text, image and video input and supports tool calling, function calling, structured outputs, web search, long context and thinking.

Can I call Mistral Large 4.0 and Qwen3.8 Max with the same API key?

Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.

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