Agentic
Like-for-like- GPT-5.6 Luna
- 84.1
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 29.1
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Luna leads
Model comparison
GPT-5.6 Luna vs Nemotron 3.5 Lightning 30B A3B NVFP4
Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Nemotron 3.5 Lightning 30B A3B NVFP4
NVIDIA
26.9/100
Estimated · Public rank #204
90% interval 17.1–36.8
GPT-5.6 Luna has the higher public score, 66.77 versus 26.93, and the 90% score intervals do not overlap.
5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna 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
No shared weighted benchmark basis supports a winner.
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.
17 GPT-5.6 Luna only
5 Shared
8 Nemotron 3.5 Lightning 30B A3B NVFP4 only
- Shared results
- 5
- GPT-5.6 Luna only
- 17
- Nemotron 3.5 Lightning 30B A3B NVFP4 only
- 8
- Like-for-like categories
- 1 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Knowledge
Directional only- GPT-5.6 Luna
- 92.3
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 80.5
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.6 Luna
- 62.7
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 42.1
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Luna
- 59.5
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 73.6
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not measured
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Luna
- 78.4
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not measured
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 72.9
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Nemotron 3.5 Lightning 30B A3B NVFP4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 84.1 | 29.1 | Like-for-like2 vs 2 rows | GPT-5.6 Luna leads |
| Knowledge | 92.3 | 80.5 | Directional only1 vs 2 rows | Directional only |
| Coding | 62.7 | 42.1 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | 59.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 73.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 78.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | 72.9 | Not comparable0 vs 1 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
GPT-5.6 Luna: 84.7%Nemotron 3.5 Lightning 30B A3B NVFP4: 23.5%Normalized gap 61.2GPT-5.6 Luna source Nemotron 3.5 Lightning 30B A3B NVFP4 sourceBrowseComp
Agentic
GPT-5.6 Luna: 83.3%Nemotron 3.5 Lightning 30B A3B NVFP4: 36.8%Normalized gap 46.5GPT-5.6 Luna source Nemotron 3.5 Lightning 30B A3B NVFP4 sourceGPQA
Knowledge
GPT-5.6 Luna: 92.3%Nemotron 3.5 Lightning 30B A3B NVFP4: 75.6%Normalized gap 16.7GPT-5.6 Luna source Nemotron 3.5 Lightning 30B A3B NVFP4 source
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
- GPT-5.6 Luna
- $0.0008
- Fits in one request
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Self-hosted; infrastructure cost varies
- Fits in one request
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Self-hosted; infrastructure cost varies
- Fits in one request
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Nemotron 3.5 Lightning 30B A3B NVFP4 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.
SpecificationGPT-5.6 LunaNemotron 3.5 Lightning 30B A3B NVFP4
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogNemotron 3.5 Lightning 30B A3B NVFP4
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogNemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingNemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardDocumented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogNemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Documented outputs
GPT-5.6 Luna
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogNemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
License
GPT-5.6 Luna
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Release date
GPT-5.6 Luna
2026-07-09
Nemotron 3.5 Lightning 30B A3B NVFP4
2026-08-11
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
- GPT-5.6 Luna has the higher public score, 66.77 versus 26.93, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
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 evidence30 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceNemotron 3.5 Lightning 30B A3B NVFP423.5%SourceGPT-5.6 Luna leads this result
BrowseComp
GPT-5.6 Luna83.3%SourceNemotron 3.5 Lightning 30B A3B NVFP436.8%SourceGPT-5.6 Luna leads this result
OSWorld 2.0
GPT-5.6 Luna45.6%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
PinchBench
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP483.4%SourceNot directly comparable
τ³-bench results
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP49.5%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceNemotron 3.5 Lightning 30B A3B NVFP423.5%SourceGPT-5.6 Luna leads this result
deepSwe
GPT-5.6 Luna67.2%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
SWE-bench Verified
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP452.8%SourceNot directly comparable
SWE Multilingual
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP436.5%SourceNot directly comparable
SciCode
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP431.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceNemotron 3.5 Lightning 30B A3B NVFP475.6%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceNemotron 3.5 Lightning 30B A3B NVFP475.6%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
HLE w/o tools
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP410.5%SourceNot directly comparable
MMLU-Pro
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP481.6%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Luna78.6%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Multimodal
Instruction following
IFBench
GPT-5.6 Luna—Nemotron 3.5 Lightning 30B A3B NVFP472.9%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Luna or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.6 Luna has the higher public score, 66.77 versus 26.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.6 Luna or Nemotron 3.5 Lightning 30B A3B NVFP4?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.6 Luna or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.6 Luna leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GPT-5.6 Luna or Nemotron 3.5 Lightning 30B A3B NVFP4?
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, GPT-5.6 Luna or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 11, 2026
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