Coding
Like-for-like- Gemini 2.5 Pro
- 31.7
- Supported · #134/151
- Laguna M.1
- 29.7
- Supported · #140/151
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Gemini 2.5 Pro leads · intervals overlap
Gemini 2.5 Pro vs Laguna M.1
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 2.5 Pro has the higher public score, 57.12 versus 3.93, and the 90% score intervals do not overlap.
1 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
Gemini 2.5 Pro
Gemini 2.5 Pro leads on the public coding lane, 31.7 to 29.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 2.5 Pro
Gemini 2.5 Pro 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
Gemini 2.5 Pro and Laguna M.1 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.
6 Gemini 2.5 Pro only
1 Shared
9 Laguna M.1 only
- Shared results
- 1
- Gemini 2.5 Pro only
- 6
- Laguna M.1 only
- 9
- 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- Gemini 2.5 Pro
- 47.7
- Estimated · #68/152
- Laguna M.1
- 23.9
- Estimated · #147/152
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 2.5 Pro
- 50.6
- Supported · #76/183
- Laguna M.1
- 18.6
- Estimated · #182/183
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 2.5 Pro
- 68.4
- Unranked · 2 rankable rows
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 35.2
- Unranked · 2 rankable rows
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not ranked
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- 70.3
- Unranked · 1 rankable row
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Pro
- 57.9
- #74/123
- Laguna M.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | Laguna M.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 31.7Supported · #134/151 | 29.7Supported · #140/151 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | Gemini 2.5 Pro leads · intervals overlap |
| Agentic | 47.7Estimated · #68/152 | 23.9Estimated · #147/152 | Directional onlyBenchAlign lane · 1 vs 2 public rows | Directional only |
| Knowledge | 50.6Supported · #76/183 | 18.6Estimated · #182/183 | Directional onlyBenchAlign lane · 2 vs 2 public rows | Directional only |
| Reasoning | 68.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 35.2Unranked · 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 | 70.3Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 57.9#74/123 | 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.
SWE-bench Verified
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
- Gemini 2.5 Pro
- $0.00625
- 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
- Gemini 2.5 Pro
- $0.0925
- 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
- Gemini 2.5 Pro
- $0.15
- 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.
SpecificationGemini 2.5 ProLaguna M.1
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
Laguna M.1
256K
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingLaguna 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.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingLaguna M.1
No comparable hosted API rate
Documented inputs
Gemini 2.5 Pro
Not sourced
Laguna M.1
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
Laguna M.1
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
Laguna M.1
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
Laguna M.1
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
Laguna M.1
Proprietary
License
Gemini 2.5 Pro
Proprietary
Laguna M.1
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
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
- Gemini 2.5 Pro has the higher public score, 57.12 versus 3.93, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Gemini 2.5 Pro 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 evidence16 rows
Agentic
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceLaguna M.174.6%SourceLaguna M.1 leads this result
Vibe Code Bench
Gemini 2.5 Pro0.40%SourceLaguna M.1—Not directly comparable
SWE Multilingual
Gemini 2.5 Pro—Laguna M.163.1%SourceNot directly comparable
SWE-bench Pro
Gemini 2.5 Pro—Laguna M.149.2%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 2.5 Pro—Laguna M.145.8%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 2.5 Pro—Laguna M.168.1%SourceNot directly comparable
SWE-bench (Vals)
Gemini 2.5 Pro—Laguna M.157.6%SourceNot directly comparable
Knowledge
Math
Frequently asked questions
Which is better, Gemini 2.5 Pro or Laguna M.1?
Gemini 2.5 Pro has the higher public score, 57.12 versus 3.93, 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, Gemini 2.5 Pro or Laguna M.1?
Gemini 2.5 Pro leads the public coding lane, 31.7 to 29.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 2.5 Pro or Laguna M.1?
Gemini 2.5 Pro scores higher for agentic tasks on the public lane, 47.7 to 23.9. Gemini 2.5 Pro and Laguna M.1 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, Gemini 2.5 Pro 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, Gemini 2.5 Pro or Laguna M.1?
Gemini 2.5 Pro has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 10, 2026
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