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Model comparison

Qwen3.7 Plus vs Ultravox v0.4.1 Llama 3.1 8B

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

Qwen3.7 Plus

Alibaba

66.2/100

Supported · Public rank #27

90% interval 56.8–75.5

Ultravox v0.4.1 Llama 3.1 8B

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
Qwen3.7 Plus only
51
Ultravox v0.4.1 Llama 3.1 8B 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
Qwen3.7 Plus
71.7
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Qwen3.7 Plus
75.6
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.7 Plus
91.7
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.7 Plus
60.1
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.7 Plus
92.9
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.7 Plus
85.4
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.7 Plus
81.5
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.7 Plus
84.5
Ultravox v0.4.1 Llama 3.1 8B
Not measured
Weighted basis
2 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

Qwen3.7 Plus
API rate not published
Fits in one request
Ultravox v0.4.1 Llama 3.1 8B
API rate not published
Fit state unavailable

Qwen3.7 Plus has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.7 Plus
API rate not published
Fits in one request
Ultravox v0.4.1 Llama 3.1 8B
API rate not published
Fit state unavailable

Qwen3.7 Plus has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.7 Plus
API rate not published
Fits in one request
Cached-input rate unavailable
Ultravox v0.4.1 Llama 3.1 8B
API rate not published
Fit state unavailable
Cached-input rate unavailable

Qwen3.7 Plus has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B 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.

Qwen3.7 Plus

1M

Ultravox v0.4.1 Llama 3.1 8B

N/A

API model ID

Qwen3.7 Plus

Not sourced

Ultravox v0.4.1 Llama 3.1 8B

Not sourced

Cached-input rate

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

Qwen3.7 Plus

No comparable hosted API rate

Ultravox v0.4.1 Llama 3.1 8B

No comparable hosted API rate

Fixie AI model documentation

Documented inputs

Qwen3.7 Plus

Not sourced

Ultravox v0.4.1 Llama 3.1 8B

Not sourced

Documented outputs

Qwen3.7 Plus

Not sourced

Ultravox v0.4.1 Llama 3.1 8B

Not sourced

Provider availability

Qwen3.7 Plus

Not sourced

Ultravox v0.4.1 Llama 3.1 8B

Not sourced

Reasoning profile

Qwen3.7 Plus

Reasoning

Ultravox v0.4.1 Llama 3.1 8B

Non-Reasoning

Weight access

Qwen3.7 Plus

Proprietary

Ultravox v0.4.1 Llama 3.1 8B

Open Weight

License

Qwen3.7 Plus

Proprietary

Ultravox v0.4.1 Llama 3.1 8B

Open Weight

Release date

Qwen3.7 Plus

2026-06-03

Ultravox v0.4.1 Llama 3.1 8B

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.

Benchmark evidence

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

Browse raw public benchmark evidence51 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.7 Plus70.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • QwenClawBench

    Qwen3.7 Plus61.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • Claw-Eval

    Qwen3.7 Plus62.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • BFCL v4

    Qwen3.7 Plus72.9%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MCP Atlas

    Qwen3.7 Plus73.2%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • VITA-Bench

    Qwen3.7 Plus45.6%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • DeepPlanning

    Qwen3.7 Plus62.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • OSWorld-Verified

    Qwen3.7 Plus73.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • AndroidWorld

    Qwen3.7 Plus81.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • OSWorld 2.0

    Qwen3.7 Plus2.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Qwen3.7 Plus70.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SWE-bench Verified

    Qwen3.7 Plus77.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SWE-bench Pro

    Qwen3.7 Plus57.6%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SWE Multilingual

    Qwen3.7 Plus75.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • NL2Repo

    Qwen3.7 Plus41.1%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SciCode

    Qwen3.7 Plus51.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • LiveCodeBench

    Qwen3.7 Plus89.6%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Reasoning

  • CritPt

    Qwen3.7 Plus9.1%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MRCRv2

    Qwen3.7 Plus91.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Knowledge

  • GPQA

    Qwen3.7 Plus90.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • GPQA-D

    Qwen3.7 Plus90.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • HLE

    Qwen3.7 Plus34.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MMLU-Pro

    Qwen3.7 Plus88.5%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MMLU-Redux

    Qwen3.7 Plus94.5%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SuperGPQA

    Qwen3.7 Plus71.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MMMLU

    Qwen3.7 Plus89.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Math

  • HMMT Feb 2026

    Qwen3.7 Plus92.9%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • IMOAnswerBench

    Qwen3.7 Plus86.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • Apex

    Qwen3.7 Plus22.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.7 Plus85.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • NOVA-63

    Qwen3.7 Plus58.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • INCLUDE

    Qwen3.7 Plus83.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MAXIFE

    Qwen3.7 Plus88.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • PolyMath

    Qwen3.7 Plus84.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Qwen3.7 Plus79%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MathVision

    Qwen3.7 Plus90.3%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • CharXiv

    Qwen3.7 Plus85.9%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • ERQA

    Qwen3.7 Plus69.8%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MedXpertQA (MM)

    Qwen3.7 Plus71.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • ScreenSpot Pro

    Qwen3.7 Plus79.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • SimpleVQA

    Qwen3.7 Plus81.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MMSearch-Plus

    Qwen3.7 Plus41.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • RealWorldQA

    Qwen3.7 Plus86.9%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • OmniDocBench 1.5

    Qwen3.7 Plus91.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • OCRBench V2

    Qwen3.7 Plus70.7%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • ODINW13

    Qwen3.7 Plus51.1%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • Video-MME (with subtitle)

    Qwen3.7 Plus88.0%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • VideoMMMU

    Qwen3.7 Plus85.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • MLVU (M-Avg)

    Qwen3.7 Plus87.4%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.7 Plus94.6%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

  • IFBench

    Qwen3.7 Plus79.1%
    Source
    Ultravox v0.4.1 Llama 3.1 8B—

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.7 Plus or Ultravox v0.4.1 Llama 3.1 8B?

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, Qwen3.7 Plus or Ultravox v0.4.1 Llama 3.1 8B?

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, Qwen3.7 Plus or Ultravox v0.4.1 Llama 3.1 8B?

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, Qwen3.7 Plus or Ultravox v0.4.1 Llama 3.1 8B?

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, Qwen3.7 Plus or Ultravox v0.4.1 Llama 3.1 8B?

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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