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Model comparison

Qwen3.6-27B vs Ultravox GLM-4P7

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Qwen3.6-27B

Alibaba

52.8/100

Estimated · Public rank #100

90% interval 41.3–64.3

Ultravox GLM-4P7

Fixie AI

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Qwen3.6-27B only
38
Ultravox GLM-4P7 only
0
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Qwen3.6-27B
59.3
Ultravox GLM-4P7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Qwen3.6-27B
77.5
Ultravox GLM-4P7
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.6-27B
Not measured
Ultravox GLM-4P7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.6-27B
53.3
Ultravox GLM-4P7
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.6-27B
89.2
Ultravox GLM-4P7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.6-27B
Not measured
Ultravox GLM-4P7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.6-27B
76.7
Ultravox GLM-4P7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.6-27B
Not measured
Ultravox GLM-4P7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Ultravox GLM-4P7
API rate not published
Fit state unavailable

Qwen3.6-27B has no comparable published API token rate. Ultravox GLM-4P7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Ultravox GLM-4P7
API rate not published
Fit state unavailable

Qwen3.6-27B has no comparable published API token rate. Ultravox GLM-4P7 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Ultravox GLM-4P7
API rate not published
Fit state unavailable
Cached-input rate unavailable

Qwen3.6-27B has no comparable published API token rate. Ultravox GLM-4P7 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Qwen3.6-27B

262K

Ultravox GLM-4P7

N/A

API model ID

Qwen3.6-27B

Not sourced

Ultravox GLM-4P7

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Qwen3.6-27B

No comparable hosted API rate

Ultravox GLM-4P7

No comparable hosted API rate

Fixie AI model documentation

Documented inputs

Qwen3.6-27B

Not sourced

Ultravox GLM-4P7

Not sourced

Documented outputs

Qwen3.6-27B

Not sourced

Ultravox GLM-4P7

Not sourced

Provider availability

Qwen3.6-27B

Not sourced

Ultravox GLM-4P7

Not sourced

Reasoning profile

Qwen3.6-27B

Reasoning

Ultravox GLM-4P7

Non-Reasoning

Weight access

Qwen3.6-27B

Open Weight

Ultravox GLM-4P7

Open Weight

License

Qwen3.6-27B

Open Weight

Ultravox GLM-4P7

Open Weight

Release date

Qwen3.6-27B

2026-04-21

Ultravox GLM-4P7

Not sourced

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Ultravox GLM-4P7
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence38 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.6-27B59.3%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • Claw-Eval

    Qwen3.6-27B72.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • QwenClawBench

    Qwen3.6-27B53.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • QwenWebBench

    Qwen3.6-27B1487
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • AndroidWorld

    Qwen3.6-27B70.3%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • Gert Labs

    Qwen3.6-27B54.84%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.6-27B77.2%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • SWE Multilingual

    Qwen3.6-27B71.3%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • SWE-bench Pro

    Qwen3.6-27B53.5%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.6-27B59.3%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • LiveCodeBench

    Qwen3.6-27B83.9%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • NL2Repo

    Qwen3.6-27B36.2%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

Knowledge

  • MMLU-Pro

    Qwen3.6-27B86.2%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • MMLU-Redux

    Qwen3.6-27B93.5%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • SuperGPQA

    Qwen3.6-27B66%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • C-Eval

    Qwen3.6-27B91.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • GPQA

    Qwen3.6-27B87.8%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • HLE

    Qwen3.6-27B24%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

Math

  • HMMT Feb 2025

    Qwen3.6-27B93.8%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • HMMT Nov 2025

    Qwen3.6-27B90.7%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • HMMT Feb 2026

    Qwen3.6-27B84.3%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • MMAnswerBench

    Qwen3.6-27B80.8%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • AIME26

    Qwen3.6-27B94.1%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

Multimodal

  • MMMU

    Qwen3.6-27B82.9%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • MMMU-Pro

    Qwen3.6-27B75.8%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • RealWorldQA

    Qwen3.6-27B84.1%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • DynaMath

    Qwen3.6-27B85.6%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • MStar

    Qwen3.6-27B81.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • SimpleVQA

    Qwen3.6-27B56.1%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • CharXiv

    Qwen3.6-27B78.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • CC-OCR

    Qwen3.6-27B81.2%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • CountBench

    Qwen3.6-27B97.8%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • RefCOCO (avg)

    Qwen3.6-27B92.5%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • ERQA

    Qwen3.6-27B62.5%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • Video-MME (with subtitle)

    Qwen3.6-27B87.7%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • VideoMMMU

    Qwen3.6-27B84.4%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • MLVU (M-Avg)

    Qwen3.6-27B86.6%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

  • V*

    Qwen3.6-27B94.7%
    Source
    Ultravox GLM-4P7—

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.6-27B or Ultravox GLM-4P7?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Qwen3.6-27B or Ultravox GLM-4P7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Qwen3.6-27B or Ultravox GLM-4P7?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Qwen3.6-27B or Ultravox GLM-4P7?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Qwen3.6-27B or Ultravox GLM-4P7?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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