Coding
Directional only- Gemini 1.5 Pro
- 35.3
- Estimated · #155/183
- Laguna S 2.1
- 47.8
- Estimated · #87/183
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Directional only
Gemini 1.5 Pro 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.
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. Gemini 1.5 Pro 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
Gemini 1.5 Pro and Laguna S 2.1 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
Gemini 1.5 Pro 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
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
- Gemini 1.5 Pro only
- 0
- 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- Gemini 1.5 Pro
- Not ranked
- Laguna S 2.1
- 48.2
- Estimated · #77/151
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 1.5 Pro
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 1.5 Pro
- 34.9
- Estimated · #166/181
- Laguna S 2.1
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Gemini 1.5 Pro
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 1.5 Pro
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 1.5 Pro
- 40.8
- Unranked · 1 rankable row
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 1.5 Pro
- Not ranked
- Laguna S 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 1.5 Pro | Laguna S 2.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 35.3Estimated · #155/183 | 47.8Estimated · #87/183 | Directional onlyBenchAlign lane · 0 vs 4 public rows | Directional only |
| Agentic | Not ranked | 48.2Estimated · #77/151 | Not comparableBenchAlign lane · 0 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 34.9Estimated · #166/181 | 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 | 40.8Unranked · 1 rankable row | 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
- Gemini 1.5 Pro
- $0.00375
- 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
- Gemini 1.5 Pro
- $0.0775
- 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
- Gemini 1.5 Pro
- $0.325
- 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
Gemini 1.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 1.5 ProLaguna S 2.1
Context window
Maximum documented context; output-token limits may be lower.
Gemini 1.5 Pro
1M
Laguna S 2.1
1M
API model ID
Gemini 1.5 Pro
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.
Gemini 1.5 Pro
Not published
Laguna S 2.1
$0.01 per 1M cached input tokens
Documented inputs
Gemini 1.5 Pro
Not sourced
Laguna S 2.1
Not sourced
Documented outputs
Gemini 1.5 Pro
Not sourced
Laguna S 2.1
Not sourced
Provider availability
Gemini 1.5 Pro
Not sourced
Laguna S 2.1
Not sourced
Reasoning profile
Gemini 1.5 Pro
Non-Reasoning
Laguna S 2.1
Reasoning
Weight access
Gemini 1.5 Pro
Proprietary
Laguna S 2.1
Open Weight
License
Gemini 1.5 Pro
Proprietary
Laguna S 2.1
Open Weight
Release date
Gemini 1.5 Pro
2024-02-15
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: $0.0775 vs $0.0056. Cache-heavy agent loop: $0.325 vs $0.006.
- Context tradeoff
- Both models list 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
Terminal-Bench 2.0
Gemini 1.5 Pro—Laguna S 2.170.2%SourceNot directly comparable
SWE Multilingual
Gemini 1.5 Pro—Laguna S 2.178.5%SourceNot directly comparable
SWE-bench Pro
Gemini 1.5 Pro—Laguna S 2.159.4%SourceNot directly comparable
deepSwe
Gemini 1.5 Pro—Laguna S 2.140.4%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 1.5 Pro 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, Gemini 1.5 Pro or Laguna S 2.1?
Laguna S 2.1 scores higher for coding on the public lane, 47.8 to 35.3. Gemini 1.5 Pro and Laguna S 2.1 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, Gemini 1.5 Pro or Laguna S 2.1?
Gemini 1.5 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 1.5 Pro or Laguna S 2.1?
For the stated presets, chat costs $0.00375 on Gemini 1.5 Pro and $0.0002 on Laguna S 2.1; repository review costs $0.0775 and $0.0056; the cache-heavy agent loop costs $0.325 and $0.006. Gemini 1.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 1.5 Pro or Laguna S 2.1?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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