Skip to main content
Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

Start free brief
BenchLM
Model A
Llama 4 Maverick

Meta

22.8/100

Supported · Public rank #212

90% interval 15.0–30.5

Llama 4 Maverick vs Qwen3.7 Max

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.7 Max

Alibaba

71.6/100

Supported · Public rank #16

90% interval 64.6–78.6

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.

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

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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
Llama 4 Maverick only
1
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
Llama 4 Maverick
Not measured
Qwen3.7 Max
69.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Llama 4 Maverick
Not measured
Qwen3.7 Max
77.9
Weighted basis
0 vs 4 rows
Reading
Not comparable

Reasoning

Not comparable
Llama 4 Maverick
Not measured
Qwen3.7 Max
90.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Llama 4 Maverick
Not measured
Qwen3.7 Max
64.2
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
Llama 4 Maverick
0.7
Qwen3.7 Max
97.1
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Llama 4 Maverick
Not measured
Qwen3.7 Max
87.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
Llama 4 Maverick
Not measured
Qwen3.7 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Llama 4 Maverick
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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Llama 4 Maverick 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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Llama 4 Maverick 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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Llama 4 Maverick 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.

Llama 4 Maverick

1M

Qwen3.7 Max

1M

API model ID

Llama 4 Maverick

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.

Llama 4 Maverick

No comparable hosted API rate

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

Llama 4 Maverick

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

Llama 4 Maverick

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

Llama 4 Maverick

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

Llama 4 Maverick

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

Llama 4 Maverick

Open Weight

Qwen3.7 Max

Proprietary

License

Llama 4 Maverick

Open Weight

Qwen3.7 Max

Proprietary

Release date

Llama 4 Maverick

2026-02-28

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
Both models list 1M.

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.

Llama 4 Maverick
API / mo$0
Self-host / mo$2,610
Break-even—
Qwen3.7 Max
API / mo$0
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 evidence36 rows

Agentic

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • QwenClawBench

    Llama 4 Maverick—
    Qwen3.7 Max64.3%
    Source

    Not directly comparable

  • Claw-Eval

    Llama 4 Maverick—
    Qwen3.7 Max65.2%
    Source

    Not directly comparable

  • BFCL v4

    Llama 4 Maverick—
    Qwen3.7 Max75.0%
    Source

    Not directly comparable

  • MCP Atlas

    Llama 4 Maverick—
    Qwen3.7 Max76.4%
    Source

    Not directly comparable

  • VITA-Bench

    Llama 4 Maverick—
    Qwen3.7 Max47.9%
    Source

    Not directly comparable

  • HLE w/ tools

    Llama 4 Maverick—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Gert Labs

    Llama 4 Maverick—
    Qwen3.7 Max64.27%
    Source

    Not directly comparable

  • ResearchClawBench

    Llama 4 Maverick—
    Qwen3.7 Max18.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Llama 4 Maverick—
    Qwen3.7 Max80.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Llama 4 Maverick—
    Qwen3.7 Max60.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Llama 4 Maverick—
    Qwen3.7 Max78.3%
    Source

    Not directly comparable

  • NL2Repo

    Llama 4 Maverick—
    Qwen3.7 Max47.2%
    Source

    Not directly comparable

  • SciCode

    Llama 4 Maverick—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • LiveCodeBench

    Llama 4 Maverick—
    Qwen3.7 Max91.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Llama 4 Maverick—
    Qwen3.7 Max90.4%
    Source

    Not directly comparable

  • CritPt

    Llama 4 Maverick—
    Qwen3.7 Max13.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Llama 4 Maverick—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • GPQA-D

    Llama 4 Maverick—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • HLE

    Llama 4 Maverick—
    Qwen3.7 Max41.4%
    Source

    Not directly comparable

  • MMLU-Pro

    Llama 4 Maverick—
    Qwen3.7 Max89.6%
    Source

    Not directly comparable

  • MMLU-Redux

    Llama 4 Maverick—
    Qwen3.7 Max95%
    Source

    Not directly comparable

  • SuperGPQA

    Llama 4 Maverick—
    Qwen3.7 Max73.6%
    Source

    Not directly comparable

  • MMMLU

    Llama 4 Maverick—
    Qwen3.7 Max90.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Llama 4 Maverick0.690%
    Source
    Qwen3.7 Max—

    Not directly comparable

  • HMMT Feb 2026

    Llama 4 Maverick—
    Qwen3.7 Max97.1%
    Source

    Not directly comparable

  • IMOAnswerBench

    Llama 4 Maverick—
    Qwen3.7 Max90.0%
    Source

    Not directly comparable

  • Apex

    Llama 4 Maverick—
    Qwen3.7 Max44.5%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Llama 4 Maverick—
    Qwen3.7 Max87%
    Source

    Not directly comparable

  • NOVA-63

    Llama 4 Maverick—
    Qwen3.7 Max59.0%
    Source

    Not directly comparable

  • INCLUDE

    Llama 4 Maverick—
    Qwen3.7 Max86.2%
    Source

    Not directly comparable

  • MAXIFE

    Llama 4 Maverick—
    Qwen3.7 Max89.2%
    Source

    Not directly comparable

  • PolyMath

    Llama 4 Maverick—
    Qwen3.7 Max86.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Llama 4 Maverick—
    Qwen3.7 Max94.3%
    Source

    Not directly comparable

  • IFBench

    Llama 4 Maverick—
    Qwen3.7 Max79.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Llama 4 Maverick 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, Llama 4 Maverick 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, Llama 4 Maverick 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, Llama 4 Maverick 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, Llama 4 Maverick or Qwen3.7 Max?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

Watch Llama 4 Maverick vs Qwen3.7 Max

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

Read a sample issue

Join 2,000+ readers.