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Comparison · 2 models · Updated Nov 2, 2026

Qwen3.8 27B vs Mistral Large 4

Too close to call on our weighted score (Mistral Large 4 67, Qwen3.8 27B 66). The right pick depends on what you value most.

  1. Alibaba (Qwen)

    Qwen3.8 27B

    Released Aug 14, 2026

    66/100
    • ECI151.8
    • Price$0.40 / $2.55
    • Context262K
    Full specs Remove Qwen3.8 27B

  2. Mistral AI

    Mistral Large 4

    Released Oct 6, 2026NewBeta

    67/100
    • ECI150.6
    • Price$0.70 / $2.10
    • Context1.05M
    Full specs Remove Mistral Large 4

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01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (Mistral Large 4 67/100, Qwen3.8 27B 66/100), so choose by what matters most for your work: Qwen3.8 27B for raw capability and Mistral Large 4 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen3.8 27BCapabilities Index (ECI): Qwen3.8 27B 151.8 · Mistral Large 4 150.6
  • Lowest priceQwen3.8 27BQwen3.8 27B $0.938 · Mistral Large 4 $1.05 per 1M tokens (3:1 blend)
  • Longest contextMistral Large 4Mistral Large 4 1,048,576 · Qwen3.8 27B 262,144 tokens
  • Widest inputsQwen3.8 27BQwen3.8 27B: Text, Images, Video · Mistral Large 4: Text, Images
  • Self-hostingQwen3.8 27BPublishes downloadable weights
How the score is built
MeasureWeightQwen3.8 27BMistral Large 4
CapabilityCapabilities Index (ECI)50%7977
Price25%4947
Inputs & features15%7070
Context window10%3761
Overall100%66/10067/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Qwen3.8 27B vs Mistral Large 4 specifications side by side
SpecificationQwen3.8 27BAlibaba (Qwen)Mistral Large 4Mistral AI
Capability
Capabilities Index (ECI)151.8 (best)150.6
ECI rank#54 of 165 (best)#57 of 165
GPQA DiamondGraduate-level science questions—88.1%
FrontierMath Tiers 1–3Research-level mathematics—58.6%
OTIS Mock AIME 2024–2025Competition mathematics—98.3%
SimpleQA VerifiedShort factual questions—20.0%
Price per million tokens
Input$0.40 (best)$0.70
Output$2.55$2.10 (best)
Cached input—$0.07
Blended (3:1)$0.938 (best)$1.05
Long-context rateSame rateSame rate
Price sourceMedian of 49 providersOfficial Mistral API
Limits
Context window262,144 tokens1,048,576 tokens (best)
Max output32,768 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesYeslow · medium · high
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenProprietary
API model ID—mistral-large-4
API providers53 (best)15
ReleasedAug 14, 2026Oct 6, 2026
Knowledge cutoff——
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Qwen3.8 27B$9.10
  • Mistral Large 4$11.20
04 — Questions

Which should you choose?

Which is better: Qwen3.8 27B or Mistral Large 4?

It is close. Our weighted score puts them within 1 points (Mistral Large 4 67/100, Qwen3.8 27B 66/100), so choose by what matters most for your work: Qwen3.8 27B for raw capability and Mistral Large 4 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3.8 27B or Mistral Large 4?

Qwen3.8 27B is cheaper at $0.40 input / $2.55 output per million tokens (median across 49 API providers). Mistral Large 4 costs $0.70 input / $2.10 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.938 per million tokens for Qwen3.8 27B versus $1.05 for Mistral Large 4 (1.1× as much).

Which scores higher on benchmarks?

Qwen3.8 27B scores higher on the Capabilities Index (ECI): Qwen3.8 27B 151.8 (#54 of 165) and Mistral Large 4 150.6 (#57 of 165). The confidence ranges of the top two overlap (149.9–153.6 vs 147.4–153.5), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3.8 27B and Mistral Large 4 yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 27B leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

Mistral Large 4 has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3.8 27B. Maximum output per response: Qwen3.8 27B up to 32,768, Mistral Large 4 up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Qwen3.8 27B accepts text, images and video; Mistral Large 4 accepts text and images. Qwen3.8 27B handles the widest range of inputs.

Are any of these open source?

Qwen3.8 27B publishes its weights and can be self-hosted; Mistral Large 4 is proprietary.

Which is newer?

Mistral Large 4 is the newest, released Oct 6, 2026. Qwen3.8 27B came out Aug 14, 2026.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.

05 — Keep comparing

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