Agentic
Directional only- Laguna XS.2
- 23.9
- Estimated · #148/151
- MAI-Thinking-1
- 51.7
- Estimated · #53/151
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Directional only
Laguna XS.2 vs MAI-Thinking-1
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MAI-Thinking-1 has the higher public score, 52.27 versus 1.9, and the 90% score intervals do not overlap.
4 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MAI-Thinking-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
Laguna XS.2 and MAI-Thinking-1 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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 Laguna XS.2 only
4 Shared
10 MAI-Thinking-1 only
- Shared results
- 4
- Laguna XS.2 only
- 6
- MAI-Thinking-1 only
- 10
- Like-for-like categories
- 0 / 8
3 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 XS.2
- 25.2
- Supported · #178/183
- MAI-Thinking-1
- 51.8
- Estimated · #60/183
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Laguna XS.2
- 18.8
- Estimated · #181/181
- MAI-Thinking-1
- 53.3
- Estimated · #68/181
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- Laguna XS.2
- Not ranked
- MAI-Thinking-1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Laguna XS.2
- Not ranked
- MAI-Thinking-1
- 73.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Laguna XS.2
- Not ranked
- MAI-Thinking-1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Laguna XS.2
- Not ranked
- MAI-Thinking-1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Laguna XS.2
- Not ranked
- MAI-Thinking-1
- 94.7
- #1/120
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Laguna XS.2 | MAI-Thinking-1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 23.9Estimated · #148/151 | 51.7Estimated · #53/151 | Directional onlyBenchAlign lane · 2 vs 1 public rows | Directional only |
| Coding | 25.2Supported · #178/183 | 51.8Estimated · #60/183 | Directional onlyBenchAlign lane · 6 vs 4 public rows | Directional only |
| Knowledge | 18.8Estimated · #181/181 | 53.3Estimated · #68/181 | Directional onlyBenchAlign lane · 2 vs 4 public rows | Directional only |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 73.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 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 | 94.7#1/120 | Not comparableProvisional lane · 0 vs 1 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-bench Pro
Coding
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
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- MAI-Thinking-1
- API rate not published
- Fits in one request
Laguna XS.2 has no comparable published API token rate. MAI-Thinking-1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- MAI-Thinking-1
- API rate not published
- Fits in one request
Laguna XS.2 has no comparable published API token rate. MAI-Thinking-1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Laguna XS.2 has no comparable published API token rate. MAI-Thinking-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.
SpecificationLaguna XS.2MAI-Thinking-1
Context window
Maximum documented context; output-token limits may be lower.
Laguna XS.2
256K
MAI-Thinking-1
256K
API model ID
Laguna XS.2
Not sourced
MAI-Thinking-1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Laguna XS.2
No comparable hosted API rate
MAI-Thinking-1
No comparable hosted API rate
Documented inputs
Laguna XS.2
Not sourced
MAI-Thinking-1
Not sourced
Documented outputs
Laguna XS.2
Not sourced
MAI-Thinking-1
Not sourced
Provider availability
Laguna XS.2
Not sourced
MAI-Thinking-1
Not sourced
Reasoning profile
Laguna XS.2
Reasoning
MAI-Thinking-1
Reasoning
Weight access
Laguna XS.2
Open Weight
MAI-Thinking-1
Proprietary
License
Laguna XS.2
Open Weight
MAI-Thinking-1
Proprietary
Release date
Laguna XS.2
2026-04-28
MAI-Thinking-1
2026-06-02
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
- MAI-Thinking-1 has the higher public score, 52.27 versus 1.9, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 256K.
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 evidence20 rows
Agentic
Coding
SWE-bench Verified
Laguna XS.269.9%SourceMAI-Thinking-173.5%SourceMAI-Thinking-1 leads this result
SWE Multilingual
Laguna XS.257.7%SourceMAI-Thinking-1—Not directly comparable
SWE-bench Pro
Laguna XS.246.3%SourceMAI-Thinking-152.8%SourceMAI-Thinking-1 leads this result
Terminal-Bench 2.0
Laguna XS.235.7%SourceMAI-Thinking-146.0%SourceMAI-Thinking-1 leads this result
LiveCodeBench (Vals)
Laguna XS.267.8%SourceMAI-Thinking-1—Not directly comparable
SWE-bench (Vals)
Laguna XS.255.2%SourceMAI-Thinking-1—Not directly comparable
LiveCodeBench v6
Laguna XS.2—MAI-Thinking-187.7%SourceNot directly comparable
Reasoning
Graphwalks BFS 128K
Laguna XS.2—MAI-Thinking-190%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Laguna XS.255.1%SourceMAI-Thinking-1—Not directly comparable
MMLU-Pro (Vals)
Laguna XS.269.1%SourceMAI-Thinking-1—Not directly comparable
GPQA
Laguna XS.2—MAI-Thinking-184.2%SourceNot directly comparable
GPQA-D
Laguna XS.2—MAI-Thinking-184.2%SourceNot directly comparable
MMLU-Pro
Laguna XS.2—MAI-Thinking-185%SourceNot directly comparable
SimpleQA
Laguna XS.2—MAI-Thinking-131%SourceNot directly comparable
Math
Instruction following
IFBench
Laguna XS.2—MAI-Thinking-185%SourceNot directly comparable
Frequently asked questions
Which is better, Laguna XS.2 or MAI-Thinking-1?
MAI-Thinking-1 has the higher public score, 52.27 versus 1.9, 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, Laguna XS.2 or MAI-Thinking-1?
MAI-Thinking-1 scores higher for coding on the public lane, 51.8 to 25.2. MAI-Thinking-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, Laguna XS.2 or MAI-Thinking-1?
MAI-Thinking-1 scores higher for agentic tasks on the public lane, 51.7 to 23.9. Laguna XS.2 and MAI-Thinking-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, Laguna XS.2 or MAI-Thinking-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, Laguna XS.2 or MAI-Thinking-1?
Both models list the same context window, 256K.
Related comparisons
Last updated September 4, 2026
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