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Model comparison

Mini-Omni 0.5B vs Qwen3.7 Max

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

Mini-Omni 0.5B

gpt-omni

—

Evidence status unavailable

90% interval unavailable

Qwen3.7 Max

Alibaba

71.8/100

Supported · Public rank #13

90% interval 65.4–78.2

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
Mini-Omni 0.5B only
0
Qwen3.7 Max only
35
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
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
69.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
77.9
Weighted basis
0 vs 4 rows
Reading
Not comparable

Reasoning

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
90.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
64.2
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
97.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
87.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Mini-Omni 0.5B
Not measured
Qwen3.7 Max
84.4
Weighted basis
0 vs 2 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

Mini-Omni 0.5B
API rate not published
Fit state unavailable
Qwen3.7 Max
API rate not published
Fits in one request

Mini-Omni 0.5B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Mini-Omni 0.5B
API rate not published
Fit state unavailable
Qwen3.7 Max
API rate not published
Fits in one request

Mini-Omni 0.5B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Mini-Omni 0.5B
API rate not published
Fit state unavailable
Cached-input rate unavailable
Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Mini-Omni 0.5B has no comparable published API token rate. Qwen3.7 Max 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.

Mini-Omni 0.5B

N/A

Qwen3.7 Max

1M

API model ID

Mini-Omni 0.5B

Not sourced

Qwen3.7 Max

Not sourced

Cached-input rate

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

Mini-Omni 0.5B

No comparable hosted API rate

gpt-omni model documentation

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

Mini-Omni 0.5B

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

Mini-Omni 0.5B

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

Mini-Omni 0.5B

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

Mini-Omni 0.5B

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

Mini-Omni 0.5B

Open Weight

Qwen3.7 Max

Proprietary

License

Mini-Omni 0.5B

Open Weight

Qwen3.7 Max

Proprietary

Release date

Mini-Omni 0.5B

2024-08-29

Qwen3.7 Max

2026-05-16

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.

Benchmark evidence

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

Browse raw public benchmark evidence35 rows

Agentic

  • Terminal-Bench 2.0

    Mini-Omni 0.5B—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • QwenClawBench

    Mini-Omni 0.5B—
    Qwen3.7 Max64.3%
    Source

    Not directly comparable

  • Claw-Eval

    Mini-Omni 0.5B—
    Qwen3.7 Max65.2%
    Source

    Not directly comparable

  • BFCL v4

    Mini-Omni 0.5B—
    Qwen3.7 Max75.0%
    Source

    Not directly comparable

  • MCP Atlas

    Mini-Omni 0.5B—
    Qwen3.7 Max76.4%
    Source

    Not directly comparable

  • VITA-Bench

    Mini-Omni 0.5B—
    Qwen3.7 Max47.9%
    Source

    Not directly comparable

  • HLE w/ tools

    Mini-Omni 0.5B—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Gert Labs

    Mini-Omni 0.5B—
    Qwen3.7 Max64.27%
    Source

    Not directly comparable

  • ResearchClawBench

    Mini-Omni 0.5B—
    Qwen3.7 Max18.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Mini-Omni 0.5B—
    Qwen3.7 Max80.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Mini-Omni 0.5B—
    Qwen3.7 Max60.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Mini-Omni 0.5B—
    Qwen3.7 Max78.3%
    Source

    Not directly comparable

  • NL2Repo

    Mini-Omni 0.5B—
    Qwen3.7 Max47.2%
    Source

    Not directly comparable

  • SciCode

    Mini-Omni 0.5B—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • LiveCodeBench

    Mini-Omni 0.5B—
    Qwen3.7 Max91.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Mini-Omni 0.5B—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Mini-Omni 0.5B—
    Qwen3.7 Max90.4%
    Source

    Not directly comparable

  • CritPt

    Mini-Omni 0.5B—
    Qwen3.7 Max13.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Mini-Omni 0.5B—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • GPQA-D

    Mini-Omni 0.5B—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • HLE

    Mini-Omni 0.5B—
    Qwen3.7 Max41.4%
    Source

    Not directly comparable

  • MMLU-Pro

    Mini-Omni 0.5B—
    Qwen3.7 Max89.6%
    Source

    Not directly comparable

  • MMLU-Redux

    Mini-Omni 0.5B—
    Qwen3.7 Max95%
    Source

    Not directly comparable

  • SuperGPQA

    Mini-Omni 0.5B—
    Qwen3.7 Max73.6%
    Source

    Not directly comparable

  • MMMLU

    Mini-Omni 0.5B—
    Qwen3.7 Max90.3%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    Mini-Omni 0.5B—
    Qwen3.7 Max97.1%
    Source

    Not directly comparable

  • IMOAnswerBench

    Mini-Omni 0.5B—
    Qwen3.7 Max90.0%
    Source

    Not directly comparable

  • Apex

    Mini-Omni 0.5B—
    Qwen3.7 Max44.5%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Mini-Omni 0.5B—
    Qwen3.7 Max87%
    Source

    Not directly comparable

  • NOVA-63

    Mini-Omni 0.5B—
    Qwen3.7 Max59.0%
    Source

    Not directly comparable

  • INCLUDE

    Mini-Omni 0.5B—
    Qwen3.7 Max86.2%
    Source

    Not directly comparable

  • MAXIFE

    Mini-Omni 0.5B—
    Qwen3.7 Max89.2%
    Source

    Not directly comparable

  • PolyMath

    Mini-Omni 0.5B—
    Qwen3.7 Max86.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Mini-Omni 0.5B—
    Qwen3.7 Max94.3%
    Source

    Not directly comparable

  • IFBench

    Mini-Omni 0.5B—
    Qwen3.7 Max79.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Mini-Omni 0.5B or Qwen3.7 Max?

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, Mini-Omni 0.5B or Qwen3.7 Max?

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, Mini-Omni 0.5B or Qwen3.7 Max?

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, Mini-Omni 0.5B or Qwen3.7 Max?

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, Mini-Omni 0.5B or Qwen3.7 Max?

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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