Coding
Like-for-like- dots3-note Preview
- 71.7
- MAI-Thinking-1
- 65.5
- Weighted basis
- 2 vs 2 rows
- Reading
- dots3-note Preview leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated August 14, 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.
4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
dots3-note Preview
dots3-note Preview leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
dots3-note Preview
dots3-note Preview has the larger documented context window.
Confidence: documented
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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
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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | dots3-note Preview | MAI-Thinking-1 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 71.7 | 65.5 | Like-for-like2 vs 2 rows | dots3-note Preview leads |
| Instruction following | 85.1 | 85.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | 83.3 | 46.0 | Not comparable1 vs 1 rows | Not comparable |
| Reasoning | 81.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 52.6 | 72.5 | Not comparable1 vs 3 rows | Not comparable |
| Math | Not measured | 89.7 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 79.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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 Pro
Coding
SWE-bench Verified
Coding
IFBench
Instruction following
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.
1K fresh input + 500 output tokens
dots3-note Preview has no comparable published API token rate. MAI-Thinking-1 has no comparable published API token rate.
50K fresh input + 3K output tokens
dots3-note Preview has no comparable published API token rate. MAI-Thinking-1 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
dots3-note Preview has no comparable published API token rate. MAI-Thinking-1 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
dots3-note Preview
MAI-Thinking-1
256K
dots3-note Preview
Not sourced
MAI-Thinking-1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
dots3-note Preview
No comparable hosted API rate
dots3-note Preview model cardMAI-Thinking-1
No comparable hosted API rate
dots3-note Preview
Not sourced
MAI-Thinking-1
Not sourced
dots3-note Preview
Not sourced
MAI-Thinking-1
Not sourced
dots3-note Preview
Not sourced
MAI-Thinking-1
Not sourced
dots3-note Preview
Reasoning
MAI-Thinking-1
Reasoning
dots3-note Preview
Open Weight
MAI-Thinking-1
Proprietary
dots3-note Preview
Open Weight
MAI-Thinking-1
Proprietary
dots3-note Preview
2026-08-14
MAI-Thinking-1
2026-06-02
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
Toolathlon-Verified
Not directly comparable
skillsBench
Not directly comparable
APEX-Agents
Not directly comparable
BrowseComp
Not directly comparable
HLE w/ tools
Not directly comparable
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Codeforces
Not directly comparable
LiveCodeBench v6
dots3-note Preview leads this result
Terminal-Bench 2.1
Not directly comparable
SWE-bench Verified
dots3-note Preview leads this result
SWE Multilingual
Not directly comparable
SWE-bench Pro
dots3-note Preview leads this result
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
HLE
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro
Not directly comparable
SimpleQA
Not directly comparable
IMOAnswerBench
Not directly comparable
AIME 2025
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
SimpleVQA
Not directly comparable
MMMU-Pro
Not directly comparable
MathVision
Not directly comparable
ZeroBench
Not directly comparable
CharXiv w/o tools
Not directly comparable
GDP.pdf (no tools)
Not directly comparable
PerceptionBench
Not directly comparable
BabyVision
Not directly comparable
MMVU
Not directly comparable
VideoMMMU
Not directly comparable
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.
dots3-note Preview leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
dots3-note Preview has the larger documented context window: 512K, compared with 256K.
Last updated August 14, 2026
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