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BenchLM
Anthropic logo
Model A
Claude Opus 4.7 (Adaptive)

Anthropic

69.1/100

Estimated · Public rank #21

90% interval 49.9–80.6

Claude Opus 4.7 (Adaptive) vs Laguna M.1

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Poolside logo
Model B
Laguna M.1

Poolside

3.93/100

Estimated · Public rank #250

90% interval 0.0–13.8

Decision reading

Claude Opus 4.7 (Adaptive) has the higher public score, 69.1 versus 3.93, and the 90% score intervals do not overlap.

4 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.

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 4.7 (Adaptive)

    Claude Opus 4.7 (Adaptive) 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

    Claude Opus 4.7 (Adaptive) is 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

    Laguna M.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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: 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
4
Claude Opus 4.7 (Adaptive) only
17
Laguna M.1 only
6
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)
57.4
Supported · #31/152
Laguna M.1
23.9
Estimated · #147/152
Basis
BenchAlign lane · 7 vs 2 public rows
Reading
Directional only

Coding

Directional only
Claude Opus 4.7 (Adaptive)
57.6
Estimated · #31/151
Laguna M.1
29.7
Supported · #140/151
Basis
BenchAlign lane · 3 vs 6 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.7 (Adaptive)
61.1
Estimated · #33/183
Laguna M.1
18.6
Estimated · #182/183
Basis
BenchAlign lane · 4 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.7 (Adaptive)
49.7
Unranked · 3 rankable rows
Laguna M.1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Laguna M.1
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
Laguna M.1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Laguna M.1
Not ranked
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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
Laguna M.1
API rate not published
Fits in one request

Laguna M.1 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
Laguna M.1
API rate not published
Fits in one request

Laguna M.1 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
Laguna M.1
API rate not published
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. Laguna M.1 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

Laguna M.1

256K

API model ID

Claude Opus 4.7 (Adaptive)

Not sourced

Laguna M.1

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

Laguna M.1

No comparable hosted API rate

Documented inputs

Claude Opus 4.7 (Adaptive)

Not sourced

Laguna M.1

Not sourced

Documented outputs

Claude Opus 4.7 (Adaptive)

Not sourced

Laguna M.1

Not sourced

Provider availability

Claude Opus 4.7 (Adaptive)

Not sourced

Laguna M.1

Not sourced

Reasoning profile

Claude Opus 4.7 (Adaptive)

Reasoning

Laguna M.1

Reasoning

Weight access

Claude Opus 4.7 (Adaptive)

Proprietary

Laguna M.1

Proprietary

License

Claude Opus 4.7 (Adaptive)

Proprietary

Laguna M.1

Proprietary

Release date

Claude Opus 4.7 (Adaptive)

2026-04-16

Laguna M.1

2026-04-28

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
Claude Opus 4.7 (Adaptive) has the higher public score, 69.1 versus 3.93, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 4.7 (Adaptive) has the larger documented window (1M).

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 evidence27 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Laguna M.145.8%
    Source

    Claude Opus 4.7 (Adaptive) leads this result

  • BrowseComp

    Claude Opus 4.7 (Adaptive)79.3%
    Source
    Laguna M.1—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.7 (Adaptive)77.3%
    Source
    Laguna M.1—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7 (Adaptive)78%
    Source
    Laguna M.1—

    Not directly comparable

  • CyberGym

    Claude Opus 4.7 (Adaptive)73.1%
    Source
    Laguna M.1—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.7 (Adaptive)18.2%
    Source
    Laguna M.1—

    Not directly comparable

  • JobBench

    Claude Opus 4.7 (Adaptive)45.9%
    Source
    Laguna M.1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 4.7 (Adaptive)—
    Laguna M.134.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.7 (Adaptive)87.6%
    Source
    Laguna M.174.6%
    Source

    Claude Opus 4.7 (Adaptive) leads this result

  • SWE-bench Pro

    Claude Opus 4.7 (Adaptive)64.3%
    Source
    Laguna M.149.2%
    Source

    Claude Opus 4.7 (Adaptive) leads this result

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Laguna M.145.8%
    Source

    Claude Opus 4.7 (Adaptive) leads this result

  • SWE Multilingual

    Claude Opus 4.7 (Adaptive)—
    Laguna M.163.1%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Opus 4.7 (Adaptive)—
    Laguna M.168.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 4.7 (Adaptive)—
    Laguna M.157.6%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 128K-256K

    Claude Opus 4.7 (Adaptive)59.2%
    Source
    Laguna M.1—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 4.7 (Adaptive)75.8%
    Source
    Laguna M.1—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 4.7 (Adaptive)0.2%
    Source
    Laguna M.1—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Laguna M.1—

    Not directly comparable

  • GPQA-D

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Laguna M.1—

    Not directly comparable

  • HLE

    Claude Opus 4.7 (Adaptive)54.7%
    Source
    Laguna M.1—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7 (Adaptive)46.9%
    Source
    Laguna M.1—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Opus 4.7 (Adaptive)—
    Laguna M.127.0%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 4.7 (Adaptive)—
    Laguna M.168.8%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Opus 4.7 (Adaptive)43.8%
    Source
    Laguna M.1—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.7 (Adaptive)43.6%
    Source
    Laguna M.1—

    Not directly comparable

  • CharXiv

    Claude Opus 4.7 (Adaptive)91%
    Source
    Laguna M.1—

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7 (Adaptive)82.1%
    Source
    Laguna M.1—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 (Adaptive) or Laguna M.1?

Claude Opus 4.7 (Adaptive) has the higher public score, 69.1 versus 3.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 4.7 (Adaptive) or Laguna M.1?

Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 57.6 to 29.7. Claude Opus 4.7 (Adaptive) is 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 Laguna M.1?

Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 57.4 to 23.9. Laguna M.1 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 Laguna M.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, Claude Opus 4.7 (Adaptive) or Laguna M.1?

Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 256K.

Related comparisons

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Last updated September 10, 2026

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