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Model comparison

Baichuan-Omni 1.5 vs Qwen3.6 Plus

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

Baichuan-Omni 1.5

Baichuan

—

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
Baichuan-Omni 1.5 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
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
61.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
70.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
62.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
57.1
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
60.5
Weighted basis
0 vs 4 rows
Reading
Not comparable

Multilingual

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
84.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
Baichuan-Omni 1.5
Not measured
Qwen3.6 Plus
79.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Baichuan-Omni 1.5
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

Baichuan-Omni 1.5
API rate not published
Fit state unavailable
Qwen3.6 Plus
API rate not published
Fits in one request

Baichuan-Omni 1.5 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

Baichuan-Omni 1.5
API rate not published
Fit state unavailable
Qwen3.6 Plus
API rate not published
Fits in one request

Baichuan-Omni 1.5 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

Baichuan-Omni 1.5
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

Baichuan-Omni 1.5 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.

Baichuan-Omni 1.5

N/A

Qwen3.6 Plus

1M

API model ID

Baichuan-Omni 1.5

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.

Baichuan-Omni 1.5

No comparable hosted API rate

Baichuan model documentation

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Baichuan-Omni 1.5

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Baichuan-Omni 1.5

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Baichuan-Omni 1.5

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Baichuan-Omni 1.5

Non-Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Baichuan-Omni 1.5

Open Weight

Qwen3.6 Plus

Proprietary

License

Baichuan-Omni 1.5

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Baichuan-Omni 1.5

2025-01-26

Qwen3.6 Plus

2026-04-02

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 evidence43 rows

Agentic

  • Terminal-Bench 2.0

    Baichuan-Omni 1.5—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Claw-Eval

    Baichuan-Omni 1.5—
    Qwen3.6 Plus58.8%
    Source

    Not directly comparable

  • QwenClawBench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus57.2%
    Source

    Not directly comparable

  • τ³-bench results

    Baichuan-Omni 1.5—
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    Baichuan-Omni 1.5—
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    Baichuan-Omni 1.5—
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    Baichuan-Omni 1.5—
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    Baichuan-Omni 1.5—
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    Baichuan-Omni 1.5—
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • Gert Labs

    Baichuan-Omni 1.5—
    Qwen3.6 Plus50.60%
    Source

    Not directly comparable

  • ResearchClawBench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Baichuan-Omni 1.5—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • SWE-bench Pro

    Baichuan-Omni 1.5—
    Qwen3.6 Plus56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Baichuan-Omni 1.5—
    Qwen3.6 Plus73.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Baichuan-Omni 1.5—
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • Vibe Code Bench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus25.56%
    Source

    Not directly comparable

Reasoning

  • AI-Needle

    Baichuan-Omni 1.5—
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    Baichuan-Omni 1.5—
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Baichuan-Omni 1.5—
    Qwen3.6 Plus90.4%
    Source

    Not directly comparable

  • SuperGPQA

    Baichuan-Omni 1.5—
    Qwen3.6 Plus71.6%
    Source

    Not directly comparable

  • MMLU-Pro

    Baichuan-Omni 1.5—
    Qwen3.6 Plus88.5%
    Source

    Not directly comparable

  • MMLU-Redux

    Baichuan-Omni 1.5—
    Qwen3.6 Plus94.5%
    Source

    Not directly comparable

  • C-Eval

    Baichuan-Omni 1.5—
    Qwen3.6 Plus93.3%
    Source

    Not directly comparable

  • HLE

    Baichuan-Omni 1.5—
    Qwen3.6 Plus28.8%
    Source

    Not directly comparable

Math

  • AIME26

    Baichuan-Omni 1.5—
    Qwen3.6 Plus95.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    Baichuan-Omni 1.5—
    Qwen3.6 Plus96.7%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Baichuan-Omni 1.5—
    Qwen3.6 Plus94.6%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Baichuan-Omni 1.5—
    Qwen3.6 Plus87.8%
    Source

    Not directly comparable

  • MMAnswerBench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus83.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Baichuan-Omni 1.5—
    Qwen3.6 Plus26.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Baichuan-Omni 1.5—
    Qwen3.6 Plus8.333%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Baichuan-Omni 1.5—
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    Baichuan-Omni 1.5—
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Baichuan-Omni 1.5—
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • MMMU-Pro

    Baichuan-Omni 1.5—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • MathVision

    Baichuan-Omni 1.5—
    Qwen3.6 Plus88.0%
    Source

    Not directly comparable

  • VideoMMMU

    Baichuan-Omni 1.5—
    Qwen3.6 Plus84.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Baichuan-Omni 1.5—
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

  • CharXiv

    Baichuan-Omni 1.5—
    Qwen3.6 Plus81.5%
    Source

    Not directly comparable

  • V*

    Baichuan-Omni 1.5—
    Qwen3.6 Plus96.9%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Baichuan-Omni 1.5—
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    Baichuan-Omni 1.5—
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Baichuan-Omni 1.5 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, Baichuan-Omni 1.5 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, Baichuan-Omni 1.5 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, Baichuan-Omni 1.5 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, Baichuan-Omni 1.5 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

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