Model A

Anthropic

70.29/100

Supported · Public rank #17

90% interval 63.9–76.7

Claude Opus 4.7 vs Laguna XS.2

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

Model B

Laguna XS.2

Poolside

1.53/100

Estimated · Public rank #251

90% interval 0.0–11.4

Decision reading

Claude Opus 4.7 has the higher public score, 70.29 versus 1.53, and the 90% score intervals do not overlap.

5 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.

Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 4.7
Claude Opus 4.7 leads on the public coding lane, 62.7 to 24.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Opus 4.7
Claude Opus 4.7 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna XS.2 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.

8 Claude Opus 4.7 only

5 Shared

5 Laguna XS.2 only

Shared results: 5
Claude Opus 4.7 only: 8
Laguna XS.2 only: 5
Like-for-like categories: 1 / 8

2 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.

Coding

Like-for-like

Claude Opus 4.7: 62.7; Supported · #14/151
Laguna XS.2: 24.7; Supported · #148/151
Basis: BenchAlign lane · 5 vs 6 public rows
Reading: Claude Opus 4.7 leads

Agentic

Directional only

Claude Opus 4.7: 55.8; Supported · #36/152
Laguna XS.2: 22.4; Estimated · #149/152
Basis: BenchAlign lane · 4 vs 2 public rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 4.7: 64.6; Estimated · #26/183
Laguna XS.2: 17.7; Estimated · #183/183
Basis: BenchAlign lane · 2 vs 2 public rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.7: Not ranked
Laguna XS.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.7: 60.8; Unranked · 2 rankable rows
Laguna XS.2: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.7: Not ranked
Laguna XS.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.7: Not ranked
Laguna XS.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.7: Not ranked
Laguna XS.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.7 and Laguna XS.2
Category	Claude Opus 4.7	Laguna XS.2	Basis	Reading
Coding	62.7Supported · #14/151	24.7Supported · #148/151	Like-for-likeBenchAlign lane · 5 vs 6 public rows	Claude Opus 4.7 leads
Agentic	55.8Supported · #36/152	22.4Estimated · #149/152	Directional onlyBenchAlign lane · 4 vs 2 public rows	Directional only
Knowledge	64.6Estimated · #26/183	17.7Estimated · #183/183	Directional onlyBenchAlign lane · 2 vs 2 public rows	Directional only
Reasoning	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	60.8Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	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.

MMLU-Pro (Vals)
Knowledge
Claude Opus 4.7: 89.9%Laguna XS.2: 69.1%Normalized gap 20.8Claude Opus 4.7 source Laguna XS.2 source
LiveCodeBench (Vals)
Coding
Claude Opus 4.7: 85.1%Laguna XS.2: 67.8%Normalized gap 17.3Claude Opus 4.7 source Laguna XS.2 source

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: $0.0175; Fits in one request
Laguna XS.2: Self-hosted; infrastructure cost varies; Fits in one request

Laguna XS.2 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7: $0.325; Fits in one request
Laguna XS.2: Self-hosted; infrastructure cost varies; Fits in one request

Laguna XS.2 has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Opus 4.7: $1.35; Fits in one request; Cached input priced at the published list-input rate
Laguna XS.2: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Non-Reasoning

Laguna XS.2

Reasoning

Weight access

Claude Opus 4.7

Proprietary

Laguna XS.2

Open Weight

License

Claude Opus 4.7

Proprietary

Laguna XS.2

Open Weight

Release date

Claude Opus 4.7

2026-04-16

Laguna XS.2

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 has the higher public score, 70.29 versus 1.53, 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 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 evidence18 rows

Agentic

Gert Labs
Claude Opus 4.765.59%
Source
Laguna XS.2—
Not directly comparable
ResearchClawBench
Claude Opus 4.720.7%
Source
Laguna XS.2—
Not directly comparable
OSWorld 2.0
Claude Opus 4.713.9%
Source
Laguna XS.2—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%
Source
Laguna XS.225.8%
Source
Claude Opus 4.7 leads this result
Terminal-Bench 2.0
Claude Opus 4.7—
Laguna XS.235.7%
Source
Not directly comparable

Coding

Vibe Code Bench
Claude Opus 4.771.00%
Source
Laguna XS.2—
Not directly comparable
React Native Evals
Claude Opus 4.782.8%
Source
Laguna XS.2—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%
Source
Laguna XS.2—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.785.1%
Source
Laguna XS.267.8%
Source
Claude Opus 4.7 leads this result
SWE-bench (Vals)
Claude Opus 4.782.0%
Source
Laguna XS.255.2%
Source
Claude Opus 4.7 leads this result
SWE-bench Verified
Claude Opus 4.7—
Laguna XS.269.9%
Source
Not directly comparable
SWE Multilingual
Claude Opus 4.7—
Laguna XS.257.7%
Source
Not directly comparable
SWE-bench Pro
Claude Opus 4.7—
Laguna XS.246.3%
Source
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—
Laguna XS.235.7%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Claude Opus 4.790.2%
Source
Laguna XS.255.1%
Source
Claude Opus 4.7 leads this result
MMLU-Pro (Vals)
Claude Opus 4.789.9%
Source
Laguna XS.269.1%
Source
Claude Opus 4.7 leads this result

Math

FrontierMath v2 (Tiers 1-3)
Claude Opus 4.743.793%
Source
Laguna XS.2—
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.722.917%
Source
Laguna XS.2—
Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 or Laguna XS.2?

Claude Opus 4.7 has the higher public score, 70.29 versus 1.53, 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 or Laguna XS.2?

Claude Opus 4.7 leads the public coding lane, 62.7 to 24.7, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Opus 4.7 or Laguna XS.2?

Claude Opus 4.7 scores higher for agentic tasks on the public lane, 55.8 to 22.4. Laguna XS.2 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 or Laguna XS.2?

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 or Laguna XS.2?

Claude Opus 4.7 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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Claude Opus 4.7 vs Laguna XS.2

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Agentic

Knowledge

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Knowledge

Math

Frequently asked questions

Which is better, Claude Opus 4.7 or Laguna XS.2?

Which is better for coding, Claude Opus 4.7 or Laguna XS.2?

Which is better for agentic tasks, Claude Opus 4.7 or Laguna XS.2?

Which costs less, Claude Opus 4.7 or Laguna XS.2?

Which has the larger context window, Claude Opus 4.7 or Laguna XS.2?

Related comparisons

Watch Claude Opus 4.7 vs Laguna XS.2