Coding
Directional only- DeepSeek V3.2
- 37.2
- Supported · #152/183
- Laguna S 2.1
- 47.8
- Estimated · #87/183
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- Directional only
DeepSeek V3.2 vs Laguna S 2.1
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
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
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 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
DeepSeek V3.2 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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. DeepSeek V3.2 does not fit this workload in one request.
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.
7 DeepSeek V3.2 only
6 Laguna S 2.1 only
- Shared results
- 0
- DeepSeek V3.2 only
- 7
- Laguna S 2.1 only
- 6
- Like-for-like categories
- 0 / 8
1 category rests 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
Not comparable- DeepSeek V3.2
- Not ranked
- Laguna S 2.1
- 48.2
- Estimated · #77/151
- Basis
- BenchAlign lane · 3 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3.2
- 51.7
- Unranked · 2 rankable rows
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3.2
- 49.7
- Estimated · #92/181
- Laguna S 2.1
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3.2
- 40.2
- Unranked · 2 rankable rows
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3.2
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3.2
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3.2
- 58.0
- #71/120
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3.2 | Laguna S 2.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 37.2Supported · #152/183 | 47.8Estimated · #87/183 | Directional onlyBenchAlign lane · 2 vs 4 public rows | Directional only |
| Agentic | Not ranked | 48.2Estimated · #77/151 | Not comparableBenchAlign lane · 3 vs 2 public rows | Not comparable |
| Reasoning | 51.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 49.7Estimated · #92/181 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | 40.2Unranked · 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 | 58.0#71/120 | 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
- DeepSeek V3.2
- $0.00049
- 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
- DeepSeek V3.2
- $0.01526
- 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
- DeepSeek V3.2
- $0.0154
- Does not fit in one request
- Laguna S 2.1
- $0.006
- Fits in one request
DeepSeek V3.2 does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V3.2Laguna S 2.1
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3.2
128K
Laguna S 2.1
1M
API model ID
DeepSeek V3.2
Not sourced
Laguna S 2.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V3.2
$0.028 per 1M cached input tokens
Laguna S 2.1
$0.01 per 1M cached input tokens
Documented inputs
DeepSeek V3.2
Not sourced
Laguna S 2.1
Not sourced
Documented outputs
DeepSeek V3.2
Not sourced
Laguna S 2.1
Not sourced
Provider availability
DeepSeek V3.2
Not sourced
Laguna S 2.1
Not sourced
Reasoning profile
DeepSeek V3.2
Non-Reasoning
Laguna S 2.1
Reasoning
Weight access
DeepSeek V3.2
Open Weight
Laguna S 2.1
Open Weight
License
DeepSeek V3.2
Open Weight
Laguna S 2.1
Open Weight
Release date
DeepSeek V3.2
2025-12-01
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.01526 vs $0.0056. Cache-heavy agent loop: $0.0154 vs $0.006.
- 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 evidence13 rows
Agentic
Claw-Eval
DeepSeek V3.240.2%SourceLaguna S 2.1—Not directly comparable
VITA-Bench
DeepSeek V3.218.5%SourceLaguna S 2.1—Not directly comparable
Gert Labs
DeepSeek V3.229.57%SourceLaguna S 2.1—Not directly comparable
Terminal-Bench 2.0
DeepSeek V3.2—Laguna S 2.170.2%SourceNot directly comparable
Toolathlon-Verified
DeepSeek V3.2—Laguna S 2.149.7%SourceNot directly comparable
Coding
SWE-Rebench
DeepSeek V3.260.9%SourceLaguna S 2.1—Not directly comparable
React Native Evals
DeepSeek V3.271.5%SourceLaguna S 2.1—Not directly comparable
Terminal-Bench 2.0
DeepSeek V3.2—Laguna S 2.170.2%SourceNot directly comparable
SWE Multilingual
DeepSeek V3.2—Laguna S 2.178.5%SourceNot directly comparable
SWE-bench Pro
DeepSeek V3.2—Laguna S 2.159.4%SourceNot directly comparable
deepSwe
DeepSeek V3.2—Laguna S 2.140.4%SourceNot directly comparable
Math
Frequently asked questions
Which is better, DeepSeek V3.2 or Laguna S 2.1?
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, DeepSeek V3.2 or Laguna S 2.1?
Laguna S 2.1 scores higher for coding on the public lane, 47.8 to 37.2. Laguna S 2.1 is 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, DeepSeek V3.2 or Laguna S 2.1?
DeepSeek V3.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V3.2 or Laguna S 2.1?
For the stated presets, chat costs $0.00049 on DeepSeek V3.2 and $0.0002 on Laguna S 2.1; repository review costs $0.01526 and $0.0056; the cache-heavy agent loop costs $0.0154 and $0.006. DeepSeek V3.2 does not fit this workload in one request.
Which has the larger context window, DeepSeek V3.2 or Laguna S 2.1?
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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