Agentic
Directional only- GPT-5.2
- 55.7
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 29.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.
See RadarModel comparison
Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
NVIDIA
26.9/100
Estimated · Public rank #204
90% interval 17.1–36.8
GPT-5.2 has the higher public score, 57.93 versus 26.93, and the 90% score intervals do not overlap.
3 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.
Prompts that approach the documented context limit
Nemotron 3.5 Lightning 30B A3B NVFP4
Nemotron 3.5 Lightning 30B A3B NVFP4 has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are 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
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
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.
3 categories use 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 | GPT-5.2 | Nemotron 3.5 Lightning 30B A3B NVFP4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 55.7 | 29.1 | Directional only2 vs 2 rows | Directional only |
| Coding | 70.6 | 42.1 | Directional only2 vs 2 rows | Directional only |
| Knowledge | 92.4 | 80.5 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 52.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 35.2 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 80.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | 72.9 | Not comparable0 vs 1 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.
BrowseComp
Agentic
SWE-bench Verified
Coding
GPQA
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
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
50K fresh input + 3K output tokens
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Nemotron 3.5 Lightning 30B A3B NVFP4 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.
GPT-5.2
400K
Nemotron 3.5 Lightning 30B A3B NVFP4
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2
Not published
Nemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardGPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.2
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
GPT-5.2
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
GPT-5.2
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
GPT-5.2
2025-12-11
Nemotron 3.5 Lightning 30B A3B NVFP4
2026-08-11
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.
BrowseComp
GPT-5.2 leads this result
OSWorld-Verified
Not directly comparable
Gert Labs
Not directly comparable
JobBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
PinchBench
Not directly comparable
τ³-bench results
Not directly comparable
SWE-bench Verified
GPT-5.2 leads this result
SWE-bench Pro
Not directly comparable
Vibe Code Bench
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SciCode
Not directly comparable
ARC-AGI-2
Not directly comparable
GPQA
GPT-5.2 leads this result
GPQA-D
Not directly comparable
HLE w/o tools
Not directly comparable
MMLU-Pro
Not directly comparable
MMMU-Pro
Not directly comparable
MathVision
Not directly comparable
CharXiv
Not directly comparable
V*
Not directly comparable
IFBench
Not directly comparable
GPT-5.2 has the higher public score, 57.93 versus 26.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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 3.5 Lightning 30B A3B NVFP4 has the larger documented context window: 1M, compared with 400K.
Last updated August 11, 2026
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