Claude Opus 4.8 vs Laguna S 2.1

Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Opus 4.8

Anthropic

69.97/100

Supported · Public rank #16

90% interval 66.2–73.8

Model B

Laguna S 2.1

Poolside

—

Evidence status unavailable

90% interval unavailable

Shared results: 4
Claude Opus 4.8 only: 37
Laguna S 2.1 only: 2
Like-for-like categories: 0 / 8

Supported: Claude Opus 4.8How the comparison works

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.

Chat turn cost
1K fresh input + 500 output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Laguna S 2.1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna S 2.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

60.7Claude Opus 4.8—Laguna S 2.1

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

SWE-bench ProCoding
Normalized gap 9.8
Claude Opus 4.8:69.2%
Laguna S 2.1:59.4%
Claude Opus 4.8 source Laguna S 2.1 source
SWE MultilingualCoding
Normalized gap 5.9
Claude Opus 4.8:84.4%
Laguna S 2.1:78.5%
Claude Opus 4.8 source Laguna S 2.1 source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable

Claude Opus 4.8: 63.3; Supported · #20/130
Laguna S 2.1: Not ranked
Basis: BenchAlign v5.8 lane · 13 vs 2 public rows
Reading: Not comparable

Coding

Not comparable

Claude Opus 4.8: 60.7; Supported · #20/151
Laguna S 2.1: Not ranked
Basis: BenchAlign v5.8 lane · 11 vs 4 public rows
Reading: Not comparable

Reasoning

Not comparable

Claude Opus 4.8: 62.8; #21/27
Laguna S 2.1: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.8: 91.2; #5/55
Laguna S 2.1: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Claude Opus 4.8: 72.0; Supported · #16/186
Laguna S 2.1: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.8: Not ranked
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.8: 75.4; #65/148
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.8: 84.4; #1/12
Laguna S 2.1: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.8 and Laguna S 2.1
Category	Claude Opus 4.8	Laguna S 2.1	Basis	Reading
Agentic	63.3Supported · #20/130	Not ranked	Not comparableBenchAlign v5.8 lane · 13 vs 2 public rows	Not comparable
Coding	60.7Supported · #20/151	Not ranked	Not comparableBenchAlign v5.8 lane · 11 vs 4 public rows	Not comparable
Reasoning	62.8#21/27	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	91.2#5/55	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Knowledge	72.0Supported · #16/186	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	75.4#65/148	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	84.4#1/12	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 4.8: $0.0175; Fits in one request
Laguna S 2.1: $0.0002; Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.8: $0.325; Fits in one request
Laguna S 2.1: $0.0056; Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 4.8: $1.35; Fits in one request; Cached input priced at the published list-input rate
Laguna S 2.1: $0.006; Fits in one request

Laguna S 2.1 has the lower modeled cost

Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationClaude Opus 4.8Laguna S 2.1

Claude Opus 4.8

Reasoning

Laguna S 2.1

Reasoning

Weight access

Claude Opus 4.8

Proprietary

Laguna S 2.1

Open Weight

License

Claude Opus 4.8

Proprietary

Laguna S 2.1

Open Weight

Release date

Claude Opus 4.8

2026-05-28

Laguna S 2.1

2026-07-21

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: Repository review: $0.325 vs $0.0056. Cache-heavy agent loop: $1.35 vs $0.006.
Context tradeoff: Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 4.8 or Laguna S 2.1?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Opus 4.8 or Laguna S 2.1?

Laguna S 2.1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Opus 4.8 or Laguna S 2.1?

Laguna S 2.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 4.8 or Laguna S 2.1?

For the stated presets, chat costs $0.0175 on Claude Opus 4.8 and $0.0002 on Laguna S 2.1; repository review costs $0.325 and $0.0056; the cache-heavy agent loop costs $1.35 and $0.006. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.8 or Laguna S 2.1?

Both models list the same context window, 1M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 4.8

API / mo$22,500

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Laguna S 2.1

API / mo$225

Self-host / mo$3,495

Break-even896M/day

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 evidence43 rows

Agentic

Terminal-Bench 3.0
Claude Opus 4.821.1%
Source
Laguna S 2.1—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.874.6%
Source
Laguna S 2.170.2%
Source
Claude Opus 4.8 leads this result
BrowseComp
Claude Opus 4.884.3%
Source
Laguna S 2.1—
Not directly comparable
DeepSearchQA
Claude Opus 4.893.1%
Source
Laguna S 2.1—
Not directly comparable
OSWorld-Verified
Claude Opus 4.883.4%
Source
Laguna S 2.1—
Not directly comparable
Finance Agent v2
Claude Opus 4.853.9%
Source
Laguna S 2.1—
Not directly comparable
MCP Atlas
Claude Opus 4.882.2%
Source
Laguna S 2.1—
Not directly comparable
Toolathlon
Claude Opus 4.859.9%
Source
Laguna S 2.1—
Not directly comparable
Gert Labs
Claude Opus 4.872.97%
Source
Laguna S 2.1—
Not directly comparable
ResearchClawBench
Claude Opus 4.821.1%
Source
Laguna S 2.1—
Not directly comparable
OSWorld 2.0
Claude Opus 4.820.6%
Source
Laguna S 2.1—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.871.9%
Source
Laguna S 2.1—
Not directly comparable
MazeBench
Claude Opus 4.80%
Source
Laguna S 2.1—
Not directly comparable
Toolathlon-Verified
Claude Opus 4.8—
Laguna S 2.149.7%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.888.6%
Source
Laguna S 2.1—
Not directly comparable
SWE-bench Pro
Claude Opus 4.869.2%
Source
Laguna S 2.159.4%
Source
Claude Opus 4.8 leads this result
SWE Multilingual
Claude Opus 4.884.4%
Source
Laguna S 2.178.5%
Source
Claude Opus 4.8 leads this result
SWE Multimodal
Claude Opus 4.838.4%
Source
Laguna S 2.1—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.874.6%
Source
Laguna S 2.170.2%
Source
Claude Opus 4.8 leads this result
cursorBench31
Claude Opus 4.858.4%
Source
Laguna S 2.1—
Not directly comparable
CursorBench 3.2
Claude Opus 4.862.3%
Source
Laguna S 2.1—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.846.5%
Source
Laguna S 2.1—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.887.8%
Source
Laguna S 2.1—
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.888.6%
Source
Laguna S 2.1—
Not directly comparable
PostTrainBench v1.1
Claude Opus 4.832.9%
Source
Laguna S 2.1—
Not directly comparable
DeepSWE
Claude Opus 4.8—
Laguna S 2.140.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Claude Opus 4.872.1%
Source
Laguna S 2.1—
Not directly comparable
ARC-AGI-3
Claude Opus 4.81.5%
Source
Laguna S 2.1—
Not directly comparable
ARC-AGI-1
Claude Opus 4.892.50%
Source
Laguna S 2.1—
Not directly comparable

Multimodal

OfficeQA Pro
Claude Opus 4.866.2%
Source
Laguna S 2.1—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.887.9%
Source
Laguna S 2.1—
Not directly comparable
CharXiv
Claude Opus 4.889.9%
Source
Laguna S 2.1—
Not directly comparable
CharXiv w/o tools
Claude Opus 4.880.5%
Source
Laguna S 2.1—
Not directly comparable

Knowledge

GPQA
Claude Opus 4.893.6%
Source
Laguna S 2.1—
Not directly comparable
GPQA-D
Claude Opus 4.893.6%
Source
Laguna S 2.1—
Not directly comparable
HLE
Claude Opus 4.857.9%
Source
Laguna S 2.1—
Not directly comparable
HLE w/o tools
Claude Opus 4.849.8%
Source
Laguna S 2.1—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.892.4%
Source
Laguna S 2.1—
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.889.6%
Source
Laguna S 2.1—
Not directly comparable

Multilingual

INCLUDE
Claude Opus 4.887.6%
Source
Laguna S 2.1—
Not directly comparable

Math

USAMO 2026
Claude Opus 4.896.7%
Source
Laguna S 2.1—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.880.000%
Source
Laguna S 2.1—
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.856.098%
Source
Laguna S 2.1—
Not directly comparable

43 public results · 4 shared

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Last updated October 19, 2026

Claude Opus 4.8 vs Laguna S 2.1

Decision reading

Which one for your work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

Which is better, Claude Opus 4.8 or Laguna S 2.1?

Which is better for coding, Claude Opus 4.8 or Laguna S 2.1?

Which is better for agentic tasks, Claude Opus 4.8 or Laguna S 2.1?

Which costs less, Claude Opus 4.8 or Laguna S 2.1?

Which has the larger context window, Claude Opus 4.8 or Laguna S 2.1?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Math

Related comparisons

Watch Claude Opus 4.8 vs Laguna S 2.1