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Comparison · 2 models · Updated Nov 2, 2026

Mistral Large 4 vs Qwen3.8 27B

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. 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

  2. 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

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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 27BMistral Large 4: Text, Images · Qwen3.8 27B: Text, Images, Video
  • Self-hostingQwen3.8 27BPublishes downloadable weights
How the score is built
MeasureWeightMistral Large 4Qwen3.8 27B
CapabilityCapabilities Index (ECI)50%7779
Price25%4749
Inputs & features15%7070
Context window10%6137
Overall100%67/10066/100
02 — Side by side

Every spec in one table

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

Mistral Large 4 vs Qwen3.8 27B specifications side by side
SpecificationMistral Large 4Mistral AIQwen3.8 27BAlibaba (Qwen)
Capability
Capabilities Index (ECI)150.6151.8 (best)
ECI rank#57 of 165#54 of 165 (best)
GPQA DiamondGraduate-level science questions88.1%—
FrontierMath Tiers 1–3Research-level mathematics58.6%—
OTIS Mock AIME 2024–2025Competition mathematics98.3%—
SimpleQA VerifiedShort factual questions20.0%—
Price per million tokens
Input$0.70$0.40 (best)
Output$2.10 (best)$2.55
Cached input$0.07—
Blended (3:1)$1.05$0.938 (best)
Long-context rateSame rateSame rate
Price sourceOfficial Mistral APIMedian of 49 providers
Limits
Context window1,048,576 tokens (best)262,144 tokens
Max output262,144 tokens (best)32,768 tokens
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoNo
VideoNoYes
ReasoningYeslow · medium · highYes
Tool callingYesYes
Structured outputYesNo
Availability
WeightsProprietaryOpen
API model IDmistral-large-4—
API providers1553 (best)
ReleasedOct 6, 2026Aug 14, 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.

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

Which should you choose?

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

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, Mistral Large 4 or Qwen3.8 27B?

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 Mistral Large 4 and Qwen3.8 27B 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: Mistral Large 4 up to 262,144, Qwen3.8 27B up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Mistral Large 4 accepts text and images; Qwen3.8 27B accepts text, images and video. 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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