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BenchLM
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Model A
Claude Opus 4.7 (Adaptive)

Anthropic

69.94/100

Estimated · Public rank #23

90% interval 51.6–81.5

Claude Opus 4.7 (Adaptive) vs Llama 4 Maverick

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

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Model B
Llama 4 Maverick

Meta

24.19/100

Supported · Public rank #227

90% interval 17.8–30.6

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Claude Opus 4.7 (Adaptive) and Llama 4 Maverick are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Llama 4 Maverick is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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: rate-fallback

  • 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
Claude Opus 4.7 (Adaptive) only
21
Llama 4 Maverick only
1
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Claude Opus 4.7 (Adaptive)
61.0
Supported · #21/151
Llama 4 Maverick
25.5
Estimated · #145/151
Basis
BenchAlign lane · 7 vs 0 public rows
Reading
Directional only

Coding

Directional only
Claude Opus 4.7 (Adaptive)
59.6
Estimated · #27/183
Llama 4 Maverick
26.8
Estimated · #175/183
Basis
BenchAlign lane · 3 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.7 (Adaptive)
61.9
Estimated · #31/181
Llama 4 Maverick
31.9
Estimated · #172/181
Basis
BenchAlign lane · 4 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.7 (Adaptive)
49.7
Unranked · 3 rankable rows
Llama 4 Maverick
57.0
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Llama 4 Maverick
25.3
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Llama 4 Maverick
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7 (Adaptive)
50.3
#36/48
Llama 4 Maverick
51.5
Unranked · 1 rankable row
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Llama 4 Maverick
50.3
#79/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Claude Opus 4.7 (Adaptive)
$0.0175
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7 (Adaptive)
$0.325
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Opus 4.7 (Adaptive)
$1.35
Fits in one request
Cached input priced at the published list-input rate
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. Llama 4 Maverick 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.

Claude Opus 4.7 (Adaptive)

1M

Llama 4 Maverick

1M

API model ID

Claude Opus 4.7 (Adaptive)

Not sourced

Llama 4 Maverick

Not sourced

Cached-input rate

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

Claude Opus 4.7 (Adaptive)

Not published

Llama 4 Maverick

No comparable hosted API rate

Documented inputs

Claude Opus 4.7 (Adaptive)

Not sourced

Llama 4 Maverick

Not sourced

Documented outputs

Claude Opus 4.7 (Adaptive)

Not sourced

Llama 4 Maverick

Not sourced

Provider availability

Claude Opus 4.7 (Adaptive)

Not sourced

Llama 4 Maverick

Not sourced

Reasoning profile

Claude Opus 4.7 (Adaptive)

Reasoning

Llama 4 Maverick

Non-Reasoning

Weight access

Claude Opus 4.7 (Adaptive)

Proprietary

Llama 4 Maverick

Open Weight

License

Claude Opus 4.7 (Adaptive)

Proprietary

Llama 4 Maverick

Open Weight

Release date

Claude Opus 4.7 (Adaptive)

2026-04-16

Llama 4 Maverick

2026-02-28

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.

Claude Opus 4.7 (Adaptive)
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Llama 4 Maverick
API / mo$0
Self-host / mo$2,610
Break-even—
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 evidence22 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • BrowseComp

    Claude Opus 4.7 (Adaptive)79.3%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.7 (Adaptive)77.3%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7 (Adaptive)78%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CyberGym

    Claude Opus 4.7 (Adaptive)73.1%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.7 (Adaptive)18.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • JobBench

    Claude Opus 4.7 (Adaptive)45.9%
    Source
    Llama 4 Maverick—

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.7 (Adaptive)87.6%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.7 (Adaptive)64.3%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

Reasoning

  • MRCR v2 128K-256K

    Claude Opus 4.7 (Adaptive)59.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 4.7 (Adaptive)75.8%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 4.7 (Adaptive)0.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • GPQA-D

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • HLE

    Claude Opus 4.7 (Adaptive)54.7%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7 (Adaptive)46.9%
    Source
    Llama 4 Maverick—

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Opus 4.7 (Adaptive)43.8%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.7 (Adaptive)—
    Llama 4 Maverick0.690%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.7 (Adaptive)43.6%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CharXiv

    Claude Opus 4.7 (Adaptive)91%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7 (Adaptive)82.1%
    Source
    Llama 4 Maverick—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 (Adaptive) or Llama 4 Maverick?

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, Claude Opus 4.7 (Adaptive) or Llama 4 Maverick?

Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 59.6 to 26.8. Claude Opus 4.7 (Adaptive) and Llama 4 Maverick are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or Llama 4 Maverick?

Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 61 to 25.5. Llama 4 Maverick is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Opus 4.7 (Adaptive) or Llama 4 Maverick?

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, Claude Opus 4.7 (Adaptive) or Llama 4 Maverick?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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