Instruction following
Like-for-like- LFM2.5-2.6B
- 38.1
- #104/124
- Nemotron 3 Nano Omni 30B A3B
- 72.4
- #62/124
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Nemotron 3 Nano Omni 30B A3B leads
Every change to the models you run, with its source and its date. Releases, price changes, retirements, API changes, and incidents.Every change to the models you run, with its source.
Follow model changesDecision reading
Nemotron 3 Nano Omni 30B A3B has the higher public score estimate, 40.89 versus 39.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
NVIDIA
40.89/100
Estimated · Public rank #184
90% interval 29.4–52.4
Updated September 18, 2026. Rank says Nemotron 3 Nano Omni 30B A3B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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
Nemotron 3 Nano Omni 30B A3B
Nemotron 3 Nano Omni 30B A3B has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LFM2.5-2.6B and Nemotron 3 Nano Omni 30B A3B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LFM2.5-2.6B and Nemotron 3 Nano Omni 30B A3B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
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
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.
Directional only · BenchAlign
Nemotron 3 Nano Omni 30B A3B scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 | LFM2.5-2.6B | Nemotron 3 Nano Omni 30B A3B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 38.1#104/124 | 72.4#62/124 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Nemotron 3 Nano Omni 30B A3B leads |
| Agentic | 37.2Estimated · #131/154 | 44.7Estimated · #91/154 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Coding | 39.2Estimated · #125/154 | 40.6Estimated · #118/154 | Directional onlyBenchAlign lane · 1 vs 2 public rows | Directional only |
| Knowledge | 37.0Estimated · #151/184 | 38.8Supported · #146/184 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Reasoning | 24.9Unranked · 2 rankable rows | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 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 |
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.
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
LFM2.5-2.6B 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
LFM2.5-2.6B 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
LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B 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.
LFM2.5-2.6B
Nemotron 3 Nano Omni 30B A3B
256K
LFM2.5-2.6B
Not sourced
Nemotron 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.
LFM2.5-2.6B
No comparable hosted API rate
LiquidAI Hugging Face model cardNemotron 3 Nano Omni 30B A3B
No comparable hosted API rate
LFM2.5-2.6B
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LFM2.5-2.6B
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LFM2.5-2.6B
Not sourced
Nemotron 3 Nano Omni 30B A3B
Not sourced
LFM2.5-2.6B
Reasoning
Nemotron 3 Nano Omni 30B A3B
Reasoning
LFM2.5-2.6B
Open Weight
Nemotron 3 Nano Omni 30B A3B
Open Weight
LFM2.5-2.6B
Open Weight
Nemotron 3 Nano Omni 30B A3B
Open Weight
LFM2.5-2.6B
2026-08-04
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.
BFCL v4
Not directly comparable
τ³-bench results
Not directly comparable
Claw-Eval
Not directly comparable
PinchBench
Not directly comparable
OSWorld
Not directly comparable
τ²-bench results
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
Nemotron 3 Nano Omni 30B A3B has the higher public score estimate, 40.89 versus 39.9, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Nemotron 3 Nano Omni 30B A3B scores higher for coding on the public lane, 40.6 to 39.2. LFM2.5-2.6B and Nemotron 3 Nano Omni 30B A3B 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.
Nemotron 3 Nano Omni 30B A3B scores higher for agentic tasks on the public lane, 44.7 to 37.2. LFM2.5-2.6B and Nemotron 3 Nano Omni 30B A3B 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Nemotron 3 Nano Omni 30B A3B has the larger documented context window: 256K, compared with 128K.
Last updated September 18, 2026
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