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Comparison · 3 models · Updated Nov 2, 2026

GLM-4.7 vs Pixtral Large (25.02) vs Qwen3 235B-A22B Thinking 2507

Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, Pixtral Large (25.02) 33). The right pick depends on what you value most.

  1. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    41/100
    • ECI143.7
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7

  2. Mistral AI

    Pixtral Large (25.02)

    Released Apr 8, 2025

    33/100
    • ECI—
    • Price$2.00 / $6.00
    • Context128K
    Full specs Remove Pixtral Large (25.02)

  3. Alibaba (Qwen)

    Qwen3 235B-A22B Thinking 2507

    Released Jul 25, 2025

    42/100
    • ECI143.8
    • Price$0.30 / $2.90
    • Context262K
    Full specs Remove Qwen3 235B-A22B Thinking 2507
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Pixtral Large (25.02) 33/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 for long inputs. 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 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Pixtral Large (25.02) 128,000 tokens
  • Widest inputsPixtral Large (25.02)GLM-4.7: Text · Pixtral Large (25.02): Text, Images · Qwen3 235B-A22B Thinking 2507: Text
  • Self-hostingGLM-4.7 and Qwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
How the score is built
MeasureWeightGLM-4.7Pixtral Large (25.02)Qwen3 235B-A22B Thinking 2507
Price50%482649
Inputs & features30%355035
Context window20%322437
Overall100%41/10033/10042/100

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.

GLM-4.7 vs Pixtral Large (25.02) vs Qwen3 235B-A22B Thinking 2507 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Pixtral Large (25.02)Mistral AIQwen3 235B-A22B Thinking 2507Alibaba (Qwen)
Capability
Capabilities Index (ECI)143.7—143.8 (best)
ECI rank#94 of 165—#93 of 165 (best)
GPQA DiamondGraduate-level science questions83.3% (best)—80.1%
OTIS Mock AIME 2024–2025Competition mathematics83.3%—86.7% (best)
SimpleQA VerifiedShort factual questions32.2%—40.4% (best)
Price per million tokens
Input$0.60$2.00$0.30 (best)
Output$2.20 (best)$6.00$2.90
Cached input$0.11——
Blended (3:1)$1.00$3.00$0.95 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 3 providersMedian of 11 providers
Limits
Context window204,800 tokens128,000 tokens262,144 tokens (best)
Max output131,072 tokens (best)8,192 tokens81,920 tokens
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesNoYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenProprietaryOpenApache 2.0
API model IDglm-4.7——
API providers31 (best)312
ReleasedDec 22, 2025Apr 8, 2025Jul 25, 2025
Knowledge cutoffApr 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.

  • GLM-4.7$10.40
  • Pixtral Large (25.02)$32.00
  • Qwen3 235B-A22B Thinking 2507$8.80
04 — Questions

Which should you choose?

Which is better: GLM-4.7, Pixtral Large (25.02) or Qwen3 235B-A22B Thinking 2507?

It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Pixtral Large (25.02) 33/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 for long inputs. 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, GLM-4.7, Pixtral Large (25.02) or Qwen3 235B-A22B Thinking 2507?

Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price); Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.00 for GLM-4.7 (1.1× as much) and $3.00 for Pixtral Large (25.02) (3.2× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.7, Pixtral Large (25.02) has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Pixtral Large (25.02) and Qwen3 235B-A22B Thinking 2507 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?

Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 128,000 for Pixtral Large (25.02). Maximum output per response: GLM-4.7 up to 131,072, Pixtral Large (25.02) up to 8,192, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Pixtral Large (25.02) accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text. Pixtral Large (25.02) handles the widest range of inputs.

Are any of these open source?

GLM-4.7 and Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Pixtral Large (25.02) is proprietary.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: GLM-4.7 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 Nov 2, 2026.

05 — Keep comparing

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