Agentic
Not comparable- Laguna S 2.1
- 48.2
- Estimated · #77/151
- Moonshot v1
- Not ranked
- Basis
- BenchAlign lane · 2 vs 0 public rows
- Reading
- Not comparable
Laguna S 2.1 vs Moonshot v1
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
Moonshot v1 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
Moonshot v1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Moonshot v1 does not fit this workload in one request. Moonshot v1 has no comparable published API token rate.
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.
6 Laguna S 2.1 only
- Shared results
- 0
- Laguna S 2.1 only
- 6
- Moonshot v1 only
- 0
- Like-for-like categories
- 0 / 8
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
Not comparable- Laguna S 2.1
- 47.8
- Estimated · #87/183
- Moonshot v1
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Laguna S 2.1
- Not ranked
- Moonshot v1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Laguna S 2.1 | Moonshot v1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 48.2Estimated · #77/151 | Not ranked | Not comparableBenchAlign lane · 2 vs 0 public rows | Not comparable |
| Coding | 47.8Estimated · #87/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public rows | Not comparable |
| 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- Moonshot v1
- API rate not published
- Fits in one request
Moonshot v1 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
- Moonshot v1
- API rate not published
- Fits in one request
Moonshot v1 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
- Moonshot v1
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
Moonshot v1 does not fit this workload in one request. Moonshot v1 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 2.1Moonshot v1
Context window
Maximum documented context; output-token limits may be lower.
Laguna S 2.1
1M
Moonshot v1
128K
API model ID
Laguna S 2.1
Not sourced
Moonshot v1
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
Moonshot v1
No comparable hosted API rate
Documented inputs
Laguna S 2.1
Not sourced
Moonshot v1
Not sourced
Documented outputs
Laguna S 2.1
Not sourced
Moonshot v1
Not sourced
Provider availability
Laguna S 2.1
Not sourced
Moonshot v1
Not sourced
Reasoning profile
Laguna S 2.1
Reasoning
Moonshot v1
Non-Reasoning
Weight access
Laguna S 2.1
Open Weight
Moonshot v1
Proprietary
License
Laguna S 2.1
Open Weight
Moonshot v1
Proprietary
Release date
Laguna S 2.1
2026-07-21
Moonshot v1
Not sourced
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- 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 evidence6 rows
Agentic
Coding
Frequently asked questions
Which is better, Laguna S 2.1 or Moonshot v1?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Laguna S 2.1 or Moonshot v1?
Moonshot v1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Laguna S 2.1 or Moonshot v1?
Moonshot v1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Laguna S 2.1 or Moonshot v1?
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 Moonshot v1?
Laguna S 2.1 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 4, 2026
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