Agentic
Directional only- GPT-5.2
- 42.9
- Supported · #104/152
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 26.1
- Estimated · #146/152
- Basis
- BenchAlign lane · 4 vs 4 public rows
- Reading
- Directional only
GPT-5.2 vs Nemotron 3.5 Lightning 30B A3B NVFP4
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
NVIDIA
21.01/100
Estimated · Public rank #239
90% interval 11.1–30.9
Decision reading
GPT-5.2 has the higher public score, 64.88 versus 21.01, and the 90% score intervals do not overlap.
3 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
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.
Long documents
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
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Nemotron 3.5 Lightning 30B A3B NVFP4 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Nemotron 3.5 Lightning 30B A3B NVFP4 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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: rate-fallback
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.
12 GPT-5.2 only
3 Shared
10 Nemotron 3.5 Lightning 30B A3B NVFP4 only
- Shared results
- 3
- GPT-5.2 only
- 12
- Nemotron 3.5 Lightning 30B A3B NVFP4 only
- 10
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Coding
Directional only- GPT-5.2
- 46.4
- Supported · #81/151
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 30.2
- Estimated · #137/151
- Basis
- BenchAlign lane · 3 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.2
- 61.7
- Supported · #30/183
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 30.2
- Estimated · #176/183
- Basis
- BenchAlign lane · 1 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.2
- 92.6
- #14/123
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 72.6
- #64/123
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.2
- 53.7
- Unranked · 3 rankable rows
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 54.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 57.5
- Unranked · 2 rankable rows
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not ranked
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2
- 66.3
- #23/48
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2 | Nemotron 3.5 Lightning 30B A3B NVFP4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 42.9Supported · #104/152 | 26.1Estimated · #146/152 | Directional onlyBenchAlign lane · 4 vs 4 public rows | Directional only |
| Coding | 46.4Supported · #81/151 | 30.2Estimated · #137/151 | Directional onlyBenchAlign lane · 3 vs 4 public rows | Directional only |
| Knowledge | 61.7Supported · #30/183 | 30.2Estimated · #176/183 | Directional onlyBenchAlign lane · 1 vs 4 public rows | Directional only |
| Instruction following | 92.6#14/123 | 72.6#64/123 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 53.7Unranked · 3 rankable rows | 54.8Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 57.5Unranked · 2 rankable rows | 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 | 66.3#23/48 | 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) 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.
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.
BrowseComp
Agentic
GPT-5.2: 65.8%Nemotron 3.5 Lightning 30B A3B NVFP4: 36.8%Normalized gap 29.0GPT-5.2 source Nemotron 3.5 Lightning 30B A3B NVFP4 sourceSWE-bench Verified
Coding
GPT-5.2: 80%Nemotron 3.5 Lightning 30B A3B NVFP4: 52.8%Normalized gap 27.2GPT-5.2 source Nemotron 3.5 Lightning 30B A3B NVFP4 sourceGPQA
Knowledge
GPT-5.2: 92.4%Nemotron 3.5 Lightning 30B A3B NVFP4: 75.6%Normalized gap 16.8GPT-5.2 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-5.2
- $0.00875
- 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-5.2
- $0.1295
- 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-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Nemotron 3.5 Lightning 30B A3B NVFP4
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.2Nemotron 3.5 Lightning 30B A3B NVFP4
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Nemotron 3.5 Lightning 30B A3B NVFP4
API model ID
GPT-5.2
Not sourced
Nemotron 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-5.2
Not published
Nemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardDocumented inputs
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Documented outputs
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Provider availability
GPT-5.2
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Reasoning profile
GPT-5.2
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
Weight access
GPT-5.2
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
License
GPT-5.2
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Release date
GPT-5.2
2025-12-11
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-5.2 has the higher public score, 64.88 versus 21.01, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Nemotron 3.5 Lightning 30B A3B NVFP4 has the larger documented window (1M).
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 evidence25 rows
Agentic
BrowseComp
GPT-5.265.8%SourceNemotron 3.5 Lightning 30B A3B NVFP436.8%SourceGPT-5.2 leads this result
OSWorld-Verified
GPT-5.247.3%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Gert Labs
GPT-5.246.54%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
JobBench
GPT-5.234.3%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Terminal-Bench 2.0
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP423.5%SourceNot directly comparable
PinchBench
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP483.4%SourceNot directly comparable
τ³-bench results
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP49.5%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceNemotron 3.5 Lightning 30B A3B NVFP452.8%SourceGPT-5.2 leads this result
SWE-bench Pro
GPT-5.255.6%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Vibe Code Bench
GPT-5.253.50%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
SWE Multilingual
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP436.5%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP423.5%SourceNot directly comparable
SciCode
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP431.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-5.252.9%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Knowledge
GPQA
GPT-5.292.4%SourceNemotron 3.5 Lightning 30B A3B NVFP475.6%SourceGPT-5.2 leads this result
GPQA-D
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP475.6%SourceNot directly comparable
HLE w/o tools
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP410.5%SourceNot directly comparable
MMLU-Pro
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP481.6%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
GPT-5.279.5%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
MathVision
GPT-5.283.0%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
CharXiv
GPT-5.282.1%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
V*
GPT-5.275.9%SourceNemotron 3.5 Lightning 30B A3B NVFP4—Not directly comparable
Instruction following
IFBench
GPT-5.2—Nemotron 3.5 Lightning 30B A3B NVFP472.9%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.2 or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.2 has the higher public score, 64.88 versus 21.01, 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-5.2 or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.2 scores higher for coding on the public lane, 46.4 to 30.2. Nemotron 3.5 Lightning 30B A3B NVFP4 is 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.
Which is better for agentic tasks, GPT-5.2 or Nemotron 3.5 Lightning 30B A3B NVFP4?
GPT-5.2 scores higher for agentic tasks on the public lane, 42.9 to 26.1. Nemotron 3.5 Lightning 30B A3B NVFP4 is 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.
Which costs less, GPT-5.2 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-5.2 or Nemotron 3.5 Lightning 30B A3B NVFP4?
Nemotron 3.5 Lightning 30B A3B NVFP4 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 10, 2026
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