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BenchLM
Model A
Qwen3.6-27B

Alibaba

53.7/100

Estimated · Public rank #105

90% interval 42.2–65.2

Qwen3.6-27B vs Toast 1

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Toast 1

Mixedbread

—

Evidence status unavailable

90% interval unavailable

Decision reading

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.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.6-27B

    Qwen3.6-27B has the larger documented context window.

    Confidence: documented

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

  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Toast 1 does not fit this workload in one request. Qwen3.6-27B has no comparable published API token rate.

    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
Toast 1 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
Toast 1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Qwen3.6-27B
77.5
Toast 1
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.6-27B
Not measured
Toast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.6-27B
53.3
Toast 1
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.6-27B
89.2
Toast 1
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.6-27B
Not measured
Toast 1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.6-27B
76.7
Toast 1
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.6-27B
Not measured
Toast 1
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
Toast 1
$0.00066
Fits in one request

Qwen3.6-27B 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
Toast 1
$0.01716
Fits in one request

Qwen3.6-27B 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
Toast 1
$0.0204
Does not fit in one request

Toast 1 does not fit this workload in one request. Qwen3.6-27B 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.

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

Toast 1

$0.036 per 1M cached input tokens

Mixedbread pricing

Reasoning profile

Qwen3.6-27B

Reasoning

Toast 1

Reasoning

Weight access

Qwen3.6-27B

Open Weight

Toast 1

Proprietary

License

Qwen3.6-27B

Open Weight

Toast 1

Proprietary

Release date

Qwen3.6-27B

2026-04-21

Toast 1

2026-08-13

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
Qwen3.6-27B has the larger documented window (262K).

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—
Toast 1
API / mo$765
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
    Toast 1—

    Not directly comparable

  • Claw-Eval

    Qwen3.6-27B72.4%
    Source
    Toast 1—

    Not directly comparable

  • QwenClawBench

    Qwen3.6-27B53.4%
    Source
    Toast 1—

    Not directly comparable

  • QwenWebBench

    Qwen3.6-27B1487
    Source
    Toast 1—

    Not directly comparable

  • AndroidWorld

    Qwen3.6-27B70.3%
    Source
    Toast 1—

    Not directly comparable

  • Gert Labs

    Qwen3.6-27B54.84%
    Source
    Toast 1—

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.6-27B77.2%
    Source
    Toast 1—

    Not directly comparable

  • SWE Multilingual

    Qwen3.6-27B71.3%
    Source
    Toast 1—

    Not directly comparable

  • SWE-bench Pro

    Qwen3.6-27B53.5%
    Source
    Toast 1—

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.6-27B59.3%
    Source
    Toast 1—

    Not directly comparable

  • LiveCodeBench

    Qwen3.6-27B83.9%
    Source
    Toast 1—

    Not directly comparable

  • NL2Repo

    Qwen3.6-27B36.2%
    Source
    Toast 1—

    Not directly comparable

Knowledge

  • MMLU-Pro

    Qwen3.6-27B86.2%
    Source
    Toast 1—

    Not directly comparable

  • MMLU-Redux

    Qwen3.6-27B93.5%
    Source
    Toast 1—

    Not directly comparable

  • SuperGPQA

    Qwen3.6-27B66%
    Source
    Toast 1—

    Not directly comparable

  • C-Eval

    Qwen3.6-27B91.4%
    Source
    Toast 1—

    Not directly comparable

  • GPQA

    Qwen3.6-27B87.8%
    Source
    Toast 1—

    Not directly comparable

  • HLE

    Qwen3.6-27B24%
    Source
    Toast 1—

    Not directly comparable

Math

  • HMMT Feb 2025

    Qwen3.6-27B93.8%
    Source
    Toast 1—

    Not directly comparable

  • HMMT Nov 2025

    Qwen3.6-27B90.7%
    Source
    Toast 1—

    Not directly comparable

  • HMMT Feb 2026

    Qwen3.6-27B84.3%
    Source
    Toast 1—

    Not directly comparable

  • MMAnswerBench

    Qwen3.6-27B80.8%
    Source
    Toast 1—

    Not directly comparable

  • AIME26

    Qwen3.6-27B94.1%
    Source
    Toast 1—

    Not directly comparable

Multimodal

  • MMMU

    Qwen3.6-27B82.9%
    Source
    Toast 1—

    Not directly comparable

  • MMMU-Pro

    Qwen3.6-27B75.8%
    Source
    Toast 1—

    Not directly comparable

  • RealWorldQA

    Qwen3.6-27B84.1%
    Source
    Toast 1—

    Not directly comparable

  • DynaMath

    Qwen3.6-27B85.6%
    Source
    Toast 1—

    Not directly comparable

  • MStar

    Qwen3.6-27B81.4%
    Source
    Toast 1—

    Not directly comparable

  • SimpleVQA

    Qwen3.6-27B56.1%
    Source
    Toast 1—

    Not directly comparable

  • CharXiv

    Qwen3.6-27B78.4%
    Source
    Toast 1—

    Not directly comparable

  • CC-OCR

    Qwen3.6-27B81.2%
    Source
    Toast 1—

    Not directly comparable

  • CountBench

    Qwen3.6-27B97.8%
    Source
    Toast 1—

    Not directly comparable

  • RefCOCO (avg)

    Qwen3.6-27B92.5%
    Source
    Toast 1—

    Not directly comparable

  • ERQA

    Qwen3.6-27B62.5%
    Source
    Toast 1—

    Not directly comparable

  • Video-MME (with subtitle)

    Qwen3.6-27B87.7%
    Source
    Toast 1—

    Not directly comparable

  • VideoMMMU

    Qwen3.6-27B84.4%
    Source
    Toast 1—

    Not directly comparable

  • MLVU (M-Avg)

    Qwen3.6-27B86.6%
    Source
    Toast 1—

    Not directly comparable

  • V*

    Qwen3.6-27B94.7%
    Source
    Toast 1—

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.6-27B or Toast 1?

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 Toast 1?

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 Toast 1?

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 Toast 1?

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 Toast 1?

Qwen3.6-27B has the larger documented context window: 262K, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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