Instruction following
Like-for-like- MAI-Thinking-1
- 94.7
- #1/121
- Mercury 2.5
- 80.1
- #53/121
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- MAI-Thinking-1 leads
MAI-Thinking-1 vs Mercury 2.5
Updated September 8, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 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
Mercury 2.5
Mercury 2.5 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
MAI-Thinking-1 and Mercury 2.5 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
MAI-Thinking-1 and Mercury 2.5 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.
12 MAI-Thinking-1 only
2 Shared
3 Mercury 2.5 only
- Shared results
- 2
- MAI-Thinking-1 only
- 12
- Mercury 2.5 only
- 3
- Like-for-like categories
- 1 / 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.
Agentic
Directional only- MAI-Thinking-1
- 50.7
- Estimated · #49/152
- Mercury 2.5
- 44.5
- Estimated · #91/152
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Directional only
Coding
Directional only- MAI-Thinking-1
- 50.8
- Estimated · #56/151
- Mercury 2.5
- 47.8
- Estimated · #73/151
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- MAI-Thinking-1
- 52.6
- Estimated · #66/182
- Mercury 2.5
- 48.3
- Estimated · #94/182
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Directional only
Reasoning
Not comparable- MAI-Thinking-1
- Not ranked
- Mercury 2.5
- 67.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 73.4
- Unranked · 3 rankable rows
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not ranked
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not ranked
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Mercury 2.5 | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 94.7#1/121 | 80.1#53/121 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | MAI-Thinking-1 leads |
| Agentic | 50.7Estimated · #49/152 | 44.5Estimated · #91/152 | Directional onlyBenchAlign lane · 1 vs 2 public rows | Directional only |
| Coding | 50.8Estimated · #56/151 | 47.8Estimated · #73/151 | Directional onlyBenchAlign lane · 4 vs 1 public rows | Directional only |
| Knowledge | 52.6Estimated · #66/182 | 48.3Estimated · #94/182 | Directional onlyBenchAlign lane · 4 vs 1 public rows | Directional only |
| Reasoning | Not ranked | 67.7Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 73.4Unranked · 3 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 |
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.
IFBench
Instruction following
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
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Mercury 2.5
- $0.00012
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Mercury 2.5
- $0.00245
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Mercury 2.5
- $0.0031
- Fits in one request
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.
SpecificationMAI-Thinking-1Mercury 2.5
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Mercury 2.5
API model ID
MAI-Thinking-1
Not sourced
Mercury 2.5
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MAI-Thinking-1
No comparable hosted API rate
Mercury 2.5
$0.004 per 1M cached input tokens
Inception models and Mercury 2.5 launch pricingDocumented inputs
MAI-Thinking-1
Not sourced
Mercury 2.5
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Mercury 2.5
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Mercury 2.5
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Mercury 2.5
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Mercury 2.5
Proprietary
License
MAI-Thinking-1
Proprietary
Mercury 2.5
Proprietary
Release date
MAI-Thinking-1
2026-06-02
Mercury 2.5
2026-09-08
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Mercury 2.5 has the larger documented window (260K).
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 evidence17 rows
Agentic
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceMercury 2.5—Not directly comparable
SWE-bench Verified
MAI-Thinking-173.5%SourceMercury 2.5—Not directly comparable
SWE-bench Pro
MAI-Thinking-152.8%SourceMercury 2.5—Not directly comparable
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceMercury 2.5—Not directly comparable
SciCode
MAI-Thinking-1—Mercury 2.538%SourceNot directly comparable
Reasoning
Graphwalks BFS 128K
MAI-Thinking-190%SourceMercury 2.5—Not directly comparable
Knowledge
Math
Instruction following
Frequently asked questions
Which is better, MAI-Thinking-1 or Mercury 2.5?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MAI-Thinking-1 or Mercury 2.5?
MAI-Thinking-1 scores higher for coding on the public lane, 50.8 to 47.8. MAI-Thinking-1 and Mercury 2.5 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, MAI-Thinking-1 or Mercury 2.5?
MAI-Thinking-1 scores higher for agentic tasks on the public lane, 50.7 to 44.5. MAI-Thinking-1 and Mercury 2.5 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, MAI-Thinking-1 or Mercury 2.5?
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, MAI-Thinking-1 or Mercury 2.5?
Mercury 2.5 has the larger documented context window: 260K, compared with 256K.
Related comparisons
Last updated September 8, 2026
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