Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MAI-Thinking-1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
MAI-Thinking-1 vs Nemotron 3 Nano Omni 30B A3B
Updated October 20, 2026. Rank says MAI-Thinking-1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
MAI-Thinking-1 has the higher public point estimate, 53.1 versus 32.11. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model B
NVIDIA
32.11/100
Estimated · Public rank #172
Conditional range 17.8–46.5
- Shared results
- 5
- MAI-Thinking-1 only
- 8
- Nemotron 3 Nano Omni 30B A3B only
- 12
- Like-for-like categories
- 1 / 8
Estimated: MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B. Conditional ranges do not establish rank confidence.How the comparison works
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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-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-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—MAI-Thinking-115.3Nemotron 3 Nano Omni 30B A3B
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 MAI-Thinking-1 only
5 Shared
12 Nemotron 3 Nano Omni 30B A3B only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
GPQAKnowledge
Normalized gap 12.0MAI-Thinking-1:84.2%Nemotron 3 Nano Omni 30B A3B:72.2%IFBenchInstruction following
Normalized gap 10.8MAI-Thinking-1:85%Nemotron 3 Nano Omni 30B A3B:74.2%MMLU-ProKnowledge
Normalized gap 7.7MAI-Thinking-1:85%Nemotron 3 Nano Omni 30B A3B:77.3%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Instruction following
Like-for-like- MAI-Thinking-1
- 94.7
- #3/149
- Nemotron 3 Nano Omni 30B A3B
- 75.4
- #67/149
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- MAI-Thinking-1 leads
Knowledge
Directional only- MAI-Thinking-1
- 55.5
- Estimated · #59/187
- Nemotron 3 Nano Omni 30B A3B
- 32.5
- Supported · #145/187
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Directional only
Agentic
Not comparable- MAI-Thinking-1
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- MAI-Thinking-1
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- 15.3
- Estimated · #135/152
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MAI-Thinking-1
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- 52.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- 43.8
- #51/55
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 81.1
- Unranked · 3 rankable rows
- Nemotron 3 Nano Omni 30B A3B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Nemotron 3 Nano Omni 30B A3B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 94.7#3/149 | 75.4#67/149 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | MAI-Thinking-1 leads |
| Knowledge | 55.5Estimated · #59/187 | 32.5Supported · #145/187 | Directional onlyBenchAlign v5.8 lane · 3 vs 3 public rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 2 public rows | Not comparable |
| Coding | Not ranked | 15.3Estimated · #135/152 | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | 52.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 43.8#51/55 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 81.1Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Nemotron 3 Nano Omni 30B A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B 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
- Nemotron 3 Nano Omni 30B A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B 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
- Nemotron 3 Nano Omni 30B A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MAI-Thinking-1 has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMAI-Thinking-1Nemotron 3 Nano Omni 30B A3B
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Nemotron 3 Nano Omni 30B A3B
256K
API model ID
MAI-Thinking-1
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
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
Nemotron 3 Nano Omni 30B A3B
No comparable hosted API rate
Documented inputs
MAI-Thinking-1
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Nemotron 3 Nano Omni 30B A3B
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Nemotron 3 Nano Omni 30B A3B
Open Weight
License
MAI-Thinking-1
Proprietary
Nemotron 3 Nano Omni 30B A3B
Open Weight
Release date
MAI-Thinking-1
2026-06-02
Nemotron 3 Nano Omni 30B A3B
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
- MAI-Thinking-1 has the higher public point estimate, 53.1 versus 32.11. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- 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.
Questions
Which is better, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?
MAI-Thinking-1 has the higher public point estimate, 53.1 versus 32.11. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?
MAI-Thinking-1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?
MAI-Thinking-1 and Nemotron 3 Nano Omni 30B A3B are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MAI-Thinking-1 or Nemotron 3 Nano Omni 30B A3B?
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 Nemotron 3 Nano Omni 30B A3B?
Both models list the same context window, 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence25 rows
Agentic
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceNemotron 3 Nano Omni 30B A3B—Not directly comparable
SWE-bench Verified
MAI-Thinking-173.5%SourceNemotron 3 Nano Omni 30B A3B—Not directly comparable
SWE-bench Pro
MAI-Thinking-152.8%SourceNemotron 3 Nano Omni 30B A3B—Not directly comparable
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceNemotron 3 Nano Omni 30B A3B—Not directly comparable
LiveCodeBench v5
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B63.2%SourceNot directly comparable
SciCode
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B32%SourceNot directly comparable
Reasoning
Graphwalks BFS 128K
MAI-Thinking-190%SourceNemotron 3 Nano Omni 30B A3B—Not directly comparable
Multimodal
ParseBench
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B48.51%SourceNot directly comparable
MMMU
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B70.8%SourceNot directly comparable
MMLongBench-Doc
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B57.5%SourceNot directly comparable
CharXiv
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B76.3%SourceNot directly comparable
ScreenSpot Pro
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B57.8%SourceNot directly comparable
Video-MME (w/o subtitle)
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B72.2%SourceNot directly comparable
AI2D_TEST
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B88.5%SourceNot directly comparable
RefCOCO (avg)
MAI-Thinking-1—Nemotron 3 Nano Omni 30B A3B90.5%SourceNot directly comparable
Knowledge
GPQA
MAI-Thinking-184.2%SourceNemotron 3 Nano Omni 30B A3B72.2%SourceMAI-Thinking-1 leads this result
GPQA-D
MAI-Thinking-184.2%SourceNemotron 3 Nano Omni 30B A3B72.2%SourceMAI-Thinking-1 leads this result
MMLU-Pro
MAI-Thinking-185%SourceNemotron 3 Nano Omni 30B A3B77.3%SourceMAI-Thinking-1 leads this result
Instruction following
Math
25 public results · 5 shared
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Last updated October 20, 2026