Agentic
Directional only- Laguna S 2.1
- 48.2
- Estimated · #77/151
- SWE-1.7
- 53.5
- Estimated · #41/151
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Directional only
Laguna S 2.1 vs SWE-1.7
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
3 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.
Long documents
Prompts that approach the documented context limit
Laguna S 2.1
Laguna S 2.1 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
Laguna S 2.1 and SWE-1.7 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
Laguna S 2.1 and SWE-1.7 are 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.
3 Laguna S 2.1 only
3 Shared
1 SWE-1.7 only
- Shared results
- 3
- Laguna S 2.1 only
- 3
- SWE-1.7 only
- 1
- Like-for-like categories
- 0 / 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
Directional only- Laguna S 2.1
- 47.8
- Estimated · #87/183
- SWE-1.7
- 55.1
- Estimated · #44/183
- Basis
- BenchAlign lane · 4 vs 3 public rows
- Reading
- Directional only
Reasoning
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Laguna S 2.1
- Not ranked
- SWE-1.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Laguna S 2.1 | SWE-1.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 48.2Estimated · #77/151 | 53.5Estimated · #41/151 | Directional onlyBenchAlign lane · 2 vs 1 public rows | Directional only |
| Coding | 47.8Estimated · #87/183 | 55.1Estimated · #44/183 | Directional onlyBenchAlign lane · 4 vs 3 public rows | Directional only |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public 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 | 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.
Terminal-Bench 2.0
Agentic
SWE Multilingual
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
- Laguna S 2.1
- $0.0002
- Fits in one request
- SWE-1.7
- API rate not published
- Fits in one request
SWE-1.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Laguna S 2.1
- $0.0056
- Fits in one request
- SWE-1.7
- API rate not published
- Fits in one request
SWE-1.7 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Laguna S 2.1
- $0.006
- Fits in one request
- SWE-1.7
- API rate not published
- Fits in one request
- Cached-input rate unavailable
SWE-1.7 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.
SpecificationLaguna S 2.1SWE-1.7
Context window
Maximum documented context; output-token limits may be lower.
Laguna S 2.1
1M
SWE-1.7
256K
API model ID
Laguna S 2.1
Not sourced
SWE-1.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Laguna S 2.1
$0.01 per 1M cached input tokens
SWE-1.7
No comparable hosted API rate
Documented inputs
Laguna S 2.1
Not sourced
SWE-1.7
Not sourced
Documented outputs
Laguna S 2.1
Not sourced
SWE-1.7
Not sourced
Provider availability
Laguna S 2.1
Not sourced
SWE-1.7
Not sourced
Reasoning profile
Laguna S 2.1
Reasoning
SWE-1.7
Reasoning
Weight access
Laguna S 2.1
Open Weight
SWE-1.7
Proprietary
License
Laguna S 2.1
Open Weight
SWE-1.7
Proprietary
Release date
Laguna S 2.1
2026-07-21
SWE-1.7
2026-07-08
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Laguna S 2.1 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 evidence7 rows
Agentic
Coding
Terminal-Bench 2.0
Laguna S 2.170.2%SourceSWE-1.781.5%SourceSWE-1.7 leads this result
SWE Multilingual
Laguna S 2.178.5%SourceSWE-1.777.8%SourceLaguna S 2.1 leads this result
SWE-bench Pro
Laguna S 2.159.4%SourceSWE-1.7—Not directly comparable
deepSwe
Laguna S 2.140.4%SourceSWE-1.7—Not directly comparable
FrontierCode 1.1 Main
Laguna S 2.1—SWE-1.742.3%SourceNot directly comparable
Frequently asked questions
Which is better, Laguna S 2.1 or SWE-1.7?
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, Laguna S 2.1 or SWE-1.7?
SWE-1.7 scores higher for coding on the public lane, 55.1 to 47.8. Laguna S 2.1 and SWE-1.7 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, Laguna S 2.1 or SWE-1.7?
SWE-1.7 scores higher for agentic tasks on the public lane, 53.5 to 48.2. Laguna S 2.1 and SWE-1.7 are 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, Laguna S 2.1 or SWE-1.7?
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, Laguna S 2.1 or SWE-1.7?
Laguna S 2.1 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 4, 2026
Watch Laguna S 2.1 vs SWE-1.7
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.