Coding
Like-for-like- Claude Sonnet 5
- 64.1
- Supported · #13/152
- Laguna M.1
- 29.8
- Supported · #141/152
- Basis
- BenchAlign lane · 11 vs 6 public rows
- Reading
- Claude Sonnet 5 leads
Claude Sonnet 5 vs Laguna M.1
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 5 has the higher public score, 69.92 versus 3.89, and the 90% score intervals do not overlap.
10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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 Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 64.1 to 29.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Sonnet 5
Claude Sonnet 5 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 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: 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.
16 Claude Sonnet 5 only
10 Shared
- Shared results
- 10
- Claude Sonnet 5 only
- 16
- Laguna M.1 only
- 0
- 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.
Agentic
Directional only- Claude Sonnet 5
- 65.9
- Supported · #11/153
- Laguna M.1
- 24.0
- Estimated · #148/153
- Basis
- BenchAlign lane · 7 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Sonnet 5
- 66.6
- Supported · #20/183
- Laguna M.1
- 18.6
- Estimated · #182/183
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 77.4
- Unranked · 2 rankable rows
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 77.5
- #13/48
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | Laguna M.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 64.1Supported · #13/152 | 29.8Supported · #141/152 | Like-for-likeBenchAlign lane · 11 vs 6 public rows | Claude Sonnet 5 leads |
| Agentic | 65.9Supported · #11/153 | 24.0Estimated · #148/153 | Directional onlyBenchAlign lane · 7 vs 2 public rows | Directional only |
| Knowledge | 66.6Supported · #20/183 | 18.6Estimated · #182/183 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Reasoning | 77.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 77.5#13/48 | Not ranked | Not comparableProvisional lane · 1 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.
Terminal-Bench 2.0
Agentic
MMLU-Pro (Vals)
Knowledge
SWE Multilingual
Coding
LiveCodeBench (Vals)
Coding
SWE-bench Pro
Coding
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 Sonnet 5
- $0.007
- 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 Sonnet 5
- $0.13
- 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 Sonnet 5
- $0.18
- Fits in one request
- Laguna M.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
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.
SpecificationClaude Sonnet 5Laguna M.1
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
Laguna M.1
256K
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewLaguna 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 Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingLaguna M.1
No comparable hosted API rate
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewLaguna M.1
Not sourced
Documented outputs
Claude Sonnet 5
Laguna M.1
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewLaguna M.1
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
Laguna M.1
Reasoning
Weight access
Claude Sonnet 5
Proprietary
Laguna M.1
Proprietary
License
Claude Sonnet 5
Proprietary
Laguna M.1
Proprietary
Release date
Claude Sonnet 5
2026-06-30
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 Sonnet 5 has the higher public score, 69.92 versus 3.89, 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 Sonnet 5 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 evidence26 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceLaguna M.1—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceLaguna M.145.8%SourceClaude Sonnet 5 leads this result
BrowseComp
Claude Sonnet 584.7%SourceLaguna M.1—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceLaguna M.1—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceLaguna M.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceLaguna M.134.1%SourceClaude Sonnet 5 leads this result
ApprenticeBench
Claude Sonnet 516%SourceLaguna M.1—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceLaguna M.174.6%SourceClaude Sonnet 5 leads this result
SWE-bench Pro
Claude Sonnet 563.2%SourceLaguna M.149.2%SourceClaude Sonnet 5 leads this result
SWE Multilingual
Claude Sonnet 578.3%SourceLaguna M.163.1%SourceClaude Sonnet 5 leads this result
SWE Multimodal
Claude Sonnet 528.1%SourceLaguna M.1—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceLaguna M.145.8%SourceClaude Sonnet 5 leads this result
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceLaguna M.1—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceLaguna M.1—Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%SourceLaguna M.1—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceLaguna M.168.1%SourceClaude Sonnet 5 leads this result
SWE-bench (Vals)
Claude Sonnet 579.6%SourceLaguna M.157.6%SourceClaude Sonnet 5 leads this result
cursorBench40
Claude Sonnet 534.1%SourceLaguna M.1—Not directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceLaguna M.1—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceLaguna M.1—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceLaguna M.1—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceLaguna M.1—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceLaguna M.127.0%SourceClaude Sonnet 5 leads this result
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceLaguna M.168.8%SourceClaude Sonnet 5 leads this result
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 5 or Laguna M.1?
Claude Sonnet 5 has the higher public score, 69.92 versus 3.89, 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 Sonnet 5 or Laguna M.1?
Claude Sonnet 5 leads the public coding lane, 64.1 to 29.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Sonnet 5 or Laguna M.1?
Claude Sonnet 5 scores higher for agentic tasks on the public lane, 65.9 to 24. 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 Sonnet 5 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 Sonnet 5 or Laguna M.1?
Claude Sonnet 5 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 14, 2026
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