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Comparison · 3 models · Updated Oct 4, 2026

Mistral Medium 3.1 vs Qwen3 VL 235B A22B Instruct vs GLM-4.5V

Qwen3 VL 235B A22B Instruct comes out ahead, 53 to 50 and 49 on our weighted score, and it is the cheaper option too.

  1. Mistral AI

    Mistral Medium 3.1

    Released Aug 12, 2025

    50/100
    • ECI—
    • Price$0.40 / $2.00
    • Context262K
    Full specs Remove Mistral Medium 3.1
  2. Our pick

    Alibaba (Qwen)

    Qwen3 VL 235B A22B Instruct

    Released Sep 23, 2025

    53/100
    • ECI—
    • Price$0.30 / $1.55
    • Context131K
    Full specs Remove Qwen3 VL 235B A22B Instruct

  3. Z.ai (Zhipu)

    GLM-4.5V

    Released Aug 11, 2025

    49/100
    • ECI—
    • Price$0.60 / $1.80
    • Context64K
    Full specs Remove GLM-4.5V
01 — Verdict

Qwen3 VL 235B A22B Instruct is our pick

Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against Mistral Medium 3.1 (50) and GLM-4.5V (49). It leads on price. Mistral Medium 3.1 wins on context window. GLM-4.5V wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceQwen3 VL 235B A22B InstructQwen3 VL 235B A22B Instruct $0.613 · Mistral Medium 3.1 $0.80 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
  • Longest contextMistral Medium 3.1Mistral Medium 3.1 262,144 · Qwen3 VL 235B A22B Instruct 131,072 · GLM-4.5V 64,000 tokens
  • Widest inputsGLM-4.5VMistral Medium 3.1: Text, Images · Qwen3 VL 235B A22B Instruct: Text, Images · GLM-4.5V: Text, Images, Video
  • Self-hostingQwen3 VL 235B A22B Instruct and GLM-4.5VPublishes downloadable weights
How the score is built
MeasureWeightMistral Medium 3.1Qwen3 VL 235B A22B InstructGLM-4.5V
Price50%546052
Inputs & features30%506070
Context window20%372412
Overall100%50/10053/10049/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

Mistral Medium 3.1 vs Qwen3 VL 235B A22B Instruct vs GLM-4.5V specifications side by side
SpecificationMistral Medium 3.1Mistral AIQwen3 VL 235B A22B InstructAlibaba (Qwen)GLM-4.5VZ.ai (Zhipu)
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.40$0.30 (best)$0.60
Output$2.00$1.55 (best)$1.80
Cached input———
Blended (3:1)$0.80$0.613 (best)$0.90
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Mistral APIMedian of 12 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)131,072 tokens64,000 tokens
Max output262,144 tokens (best)32,768 tokens16,384 tokens
Inputs and features
TextYesYesYes
ImagesYesYesYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningNoNoYes
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsProprietaryOpenOpen
API model IDmistral-medium-2508—glm-4.5v
API providers112 (best)11
ReleasedAug 12, 2025Sep 23, 2025Aug 11, 2025
Knowledge cutoffMay 2025Mar 31, 2025Apr 2025
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 Medium 3.1$8.00
  • Qwen3 VL 235B A22B Instruct$6.10
  • GLM-4.5V$9.60
04 — Questions

Which should you choose?

Which is better: Mistral Medium 3.1, Qwen3 VL 235B A22B Instruct or GLM-4.5V?

Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against Mistral Medium 3.1 (50) and GLM-4.5V (49). It leads on price. Mistral Medium 3.1 wins on context window. GLM-4.5V wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, Mistral Medium 3.1, Qwen3 VL 235B A22B Instruct or GLM-4.5V?

Qwen3 VL 235B A22B Instruct is cheaper at $0.30 input / $1.55 output per million tokens (median across 12 API providers). Mistral Medium 3.1 costs $0.40 input / $2.00 output per million tokens (official Mistral API price); GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.613 per million tokens for Qwen3 VL 235B A22B Instruct versus $0.80 for Mistral Medium 3.1 (1.3× as much) and $0.90 for GLM-4.5V (1.5× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Mistral Medium 3.1 has not been scored yet, Qwen3 VL 235B A22B Instruct has not been scored yet and GLM-4.5V has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Mistral Medium 3.1, Qwen3 VL 235B A22B Instruct and GLM-4.5V yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Mistral Medium 3.1 has the largest context window at 262,144 tokens, against 131,072 for Qwen3 VL 235B A22B Instruct and 64,000 for GLM-4.5V. Maximum output per response: Mistral Medium 3.1 up to 262,144, Qwen3 VL 235B A22B Instruct up to 32,768, GLM-4.5V up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Mistral Medium 3.1 accepts text and images; Qwen3 VL 235B A22B Instruct accepts text and images; GLM-4.5V accepts text, images and video. GLM-4.5V handles the widest range of inputs.

Are any of these open source?

Qwen3 VL 235B A22B Instruct and GLM-4.5V publishes its weights and can be self-hosted; Mistral Medium 3.1 is proprietary.

Which is newer?

Qwen3 VL 235B A22B Instruct is the newest, released Sep 23, 2025. Mistral Medium 3.1 came out Aug 12, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: Mistral Medium 3.1 May 2025, Qwen3 VL 235B A22B Instruct Mar 31, 2025, GLM-4.5V Apr 2025.

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 Oct 4, 2026.

05 — Keep comparing

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