ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs Palmyra X5 vs GLM-4.7

Palmyra X5 comes out ahead, 47 to 42 and 41 on our weighted score, though Qwen3 235B-A22B Thinking 2507 is 2.1× cheaper per token.

  1. 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
  2. Our pick

    Writer

    Palmyra X5

    Released Apr 28, 2025

    47/100
    • ECI—
    • Price$0.60 / $6.00
    • Context1M
    Full specs Remove Palmyra X5

  3. 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
01 — Verdict

Palmyra X5 is our pick

Palmyra X5 is the better all-round choice, scoring 47/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on inputs & features and context window. 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 · Palmyra X5 $1.95 per 1M tokens (3:1 blend)
  • Longest contextPalmyra X5Palmyra X5 1,000,000 · Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsPalmyra X5Qwen3 235B-A22B Thinking 2507: Text · Palmyra X5: Text, Images · GLM-4.7: Text
  • Self-hostingQwen3 235B-A22B Thinking 2507 and GLM-4.7Publishes downloadable weights (Apache 2.0)
How the score is built
MeasureWeightQwen3 235B-A22B Thinking 2507Palmyra X5GLM-4.7
Price50%493548
Inputs & features30%356035
Context window20%376032
Overall100%42/10047/10041/100

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.

Qwen3 235B-A22B Thinking 2507 vs Palmyra X5 vs GLM-4.7 specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)Palmyra X5WriterGLM-4.7Z.ai (Zhipu)
Capability
Capabilities Index (ECI)143.8 (best)—143.7
ECI rank#93 of 165 (best)—#94 of 165
GPQA DiamondGraduate-level science questions80.1%—83.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics86.7% (best)—83.3%
SimpleQA VerifiedShort factual questions40.4% (best)—32.2%
Price per million tokens
Input$0.30 (best)$0.60$0.60
Output$2.90$6.00$2.20 (best)
Cached input——$0.11
Blended (3:1)$0.95 (best)$1.95$1.00
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 4 providersOfficial Z.AI API
Limits
Context window262,144 tokens1,000,000 tokens (best)204,800 tokens
Max output81,920 tokens8,192 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0ProprietaryOpen
API model ID——glm-4.7
API providers12431 (best)
ReleasedJul 25, 2025Apr 28, 2025Dec 22, 2025
Knowledge cutoff——Apr 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.

  • Qwen3 235B-A22B Thinking 2507$8.80
  • Palmyra X5$18.00
  • GLM-4.7$10.40
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507, Palmyra X5 or GLM-4.7?

Palmyra X5 is the better all-round choice, scoring 47/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on inputs & features and context window. 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, Qwen3 235B-A22B Thinking 2507, Palmyra X5 or GLM-4.7?

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); Palmyra X5 costs $0.60 input / $6.00 output per million tokens (median across 4 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 $1.95 for Palmyra X5 (2.1× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8, Palmyra X5 has not been scored yet and GLM-4.7 has an ECI of 143.7.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, Palmyra X5 and GLM-4.7 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?

Palmyra X5 has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 204,800 for GLM-4.7. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Palmyra X5 up to 8,192, GLM-4.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; Palmyra X5 accepts text and images; GLM-4.7 accepts text. Palmyra X5 handles the widest range of inputs.

Are any of these open source?

Qwen3 235B-A22B Thinking 2507 and GLM-4.7 publishes its weights (Apache 2.0) and can be self-hosted; Palmyra X5 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; Palmyra X5 came out Apr 28, 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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