Skip to main content
BenchLM
Advertise

Model comparison

Qwen2.5-Omni 7B vs Qwen3.6 Plus

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

Qwen2.5-Omni 7B

Alibaba

—

Evidence status unavailable

90% interval unavailable

Qwen3.6 Plus

Alibaba

64.6/100

Supported · Public rank #34

90% interval 55.9–73.3

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
Qwen2.5-Omni 7B only
0
Qwen3.6 Plus only
43
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
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
61.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
70.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
62.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
57.1
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
60.5
Weighted basis
0 vs 4 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
84.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
79.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen2.5-Omni 7B
Not measured
Qwen3.6 Plus
82.3
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

Qwen2.5-Omni 7B
API rate not published
Fit state unavailable
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen2.5-Omni 7B has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen2.5-Omni 7B
API rate not published
Fit state unavailable
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen2.5-Omni 7B has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen2.5-Omni 7B
API rate not published
Fit state unavailable
Cached-input rate unavailable
Qwen3.6 Plus
API rate not published
Fits in one request
Cached-input rate unavailable

Qwen2.5-Omni 7B has no comparable published API token rate. Qwen3.6 Plus 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.

Qwen2.5-Omni 7B

N/A

Qwen3.6 Plus

1M

API model ID

Qwen2.5-Omni 7B

Not sourced

Qwen3.6 Plus

Not sourced

Cached-input rate

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

Qwen2.5-Omni 7B

No comparable hosted API rate

Alibaba model documentation

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Qwen2.5-Omni 7B

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Qwen2.5-Omni 7B

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Qwen2.5-Omni 7B

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Qwen2.5-Omni 7B

Non-Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Qwen2.5-Omni 7B

Open Weight

Qwen3.6 Plus

Proprietary

License

Qwen2.5-Omni 7B

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Qwen2.5-Omni 7B

2025-03-26

Qwen3.6 Plus

2026-04-02

If you already use one of these models
Deployment change
Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
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 evidence43 rows

Agentic

  • Terminal-Bench 2.0

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Claw-Eval

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus58.8%
    Source

    Not directly comparable

  • QwenClawBench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus57.2%
    Source

    Not directly comparable

  • τ³-bench results

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • Gert Labs

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus50.60%
    Source

    Not directly comparable

  • ResearchClawBench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • SWE-bench Pro

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus73.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • Vibe Code Bench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus25.56%
    Source

    Not directly comparable

Reasoning

  • AI-Needle

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus90.4%
    Source

    Not directly comparable

  • SuperGPQA

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus71.6%
    Source

    Not directly comparable

  • MMLU-Pro

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus88.5%
    Source

    Not directly comparable

  • MMLU-Redux

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus94.5%
    Source

    Not directly comparable

  • C-Eval

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus93.3%
    Source

    Not directly comparable

  • HLE

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus28.8%
    Source

    Not directly comparable

Math

  • AIME26

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus95.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus96.7%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus94.6%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus87.8%
    Source

    Not directly comparable

  • MMAnswerBench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus83.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus26.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus8.333%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • MMMU-Pro

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • MathVision

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus88.0%
    Source

    Not directly comparable

  • VideoMMMU

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus84.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

  • CharXiv

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus81.5%
    Source

    Not directly comparable

  • V*

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus96.9%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    Qwen2.5-Omni 7B—
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Qwen2.5-Omni 7B or Qwen3.6 Plus?

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, Qwen2.5-Omni 7B or Qwen3.6 Plus?

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, Qwen2.5-Omni 7B or Qwen3.6 Plus?

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, Qwen2.5-Omni 7B or Qwen3.6 Plus?

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, Qwen2.5-Omni 7B or Qwen3.6 Plus?

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

Watch Qwen2.5-Omni 7B vs Qwen3.6 Plus

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.