Agentic
Not comparable- LongCat-Flash-Lite-Sparse
- Not ranked
- Nemotron 3 Nano Omni 30B A3B
- 44.3
- Estimated · #90/152
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Not comparable
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Follow model changesUpdated September 10, 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
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.
Prompts that approach the documented context limit
LongCat-Flash-Lite-Sparse
LongCat-Flash-Lite-Sparse has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LongCat-Flash-Lite-Sparse is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LongCat-Flash-Lite-Sparse is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
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.
| Category | LongCat-Flash-Lite-Sparse | Nemotron 3 Nano Omni 30B A3B | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 44.3Estimated · #90/152 | Not comparableBenchAlign lane · 4 vs 2 public rows | Not comparable |
| Coding | Not ranked | 40.2Estimated · #116/151 | Not comparableBenchAlign lane · 4 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 38.7Supported · #143/183 | Not comparableBenchAlign lane · 5 vs 3 public rows | Not comparable |
| Math | Not ranked | 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 | 39.3#42/48 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 75.9#59/123 | 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.
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.
MMLU-Pro
Knowledge
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
LongCat-Flash-Lite-Sparse has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
50K fresh input + 3K output tokens
LongCat-Flash-Lite-Sparse has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
LongCat-Flash-Lite-Sparse has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B 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.
LongCat-Flash-Lite-Sparse
Nemotron 3 Nano Omni 30B A3B
256K
LongCat-Flash-Lite-Sparse
meituan-longcat/LongCat-Flash-Lite-Sparse
Meituan LongCat-Flash-Lite-Sparse model cardNemotron 3 Nano Omni 30B A3B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LongCat-Flash-Lite-Sparse
No comparable hosted API rate
Meituan LongCat-Flash-Lite-Sparse model cardNemotron 3 Nano Omni 30B A3B
No comparable hosted API rate
LongCat-Flash-Lite-Sparse
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LongCat-Flash-Lite-Sparse
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LongCat-Flash-Lite-Sparse
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LongCat-Flash-Lite-Sparse
Reasoning
Nemotron 3 Nano Omni 30B A3B
Reasoning
LongCat-Flash-Lite-Sparse
Open Weight
Nemotron 3 Nano Omni 30B A3B
Open Weight
LongCat-Flash-Lite-Sparse
Open Weight
Nemotron 3 Nano Omni 30B A3B
Open Weight
LongCat-Flash-Lite-Sparse
2026-07-31
Nemotron 3 Nano Omni 30B A3B
2026-04-28
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.
Terminal-Bench 2.0
Not directly comparable
VITA-Bench
Not directly comparable
MCP Atlas
Not directly comparable
BrowseComp
Not directly comparable
OSWorld
Not directly comparable
τ²-bench results
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench v5
Not directly comparable
SciCode
Not directly comparable
MMLU
Not directly comparable
MMLU-Pro
LongCat-Flash-Lite-Sparse leads this result
CMMLU
Not directly comparable
C-Eval
Not directly comparable
GPQA-D
Nemotron 3 Nano Omni 30B A3B leads this result
GPQA
Not directly comparable
MATH-500
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
AIME 2025
Not directly comparable
MMMU
Not directly comparable
MMLongBench-Doc
Not directly comparable
CharXiv
Not directly comparable
ScreenSpot Pro
Not directly comparable
Video-MME (w/o subtitle)
Not directly comparable
AI2D_TEST
Not directly comparable
RefCOCO (avg)
Not directly comparable
IFBench
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.
LongCat-Flash-Lite-Sparse is not ranked on the public lane for coding, so no winner is named for coding.
LongCat-Flash-Lite-Sparse is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
LongCat-Flash-Lite-Sparse has the larger documented context window: 1M, compared with 256K.
Last updated September 10, 2026
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