Updated September 24, 2026. Rank says GPT-5.5 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
GPT-5.5 has the higher public score, 69.13 versus 19.1, and the 90% score intervals do not overlap. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
NVIDIA
19.1/100
Estimated · Public rank #186
90% interval 9.2–29.0
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
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.
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.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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 v5.7
GPT-5.5 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.
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
BrowseCompAgentic
Normalized gap 47.6HLE w/o toolsKnowledge
Normalized gap 30.9GPQAKnowledge
Normalized gap 18.0Each row shows the public-lane category score for both models: the BenchAlign v5.7 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 | GPT-5.5 | Nemotron 3.5 Lightning 30B A3B NVFP4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.1Supported · #15/105 | 7.3Estimated · #104/105 | Directional onlyBenchAlign v5.7 lane · 14 vs 4 public rows | Directional only |
| Coding | 64.1Supported · #11/135 | 11.4Estimated · #135/135 | Directional onlyBenchAlign v5.7 lane · 9 vs 4 public rows | Directional only |
| Knowledge | 70.0Supported · #12/158 | 20.7Estimated · #154/158 | Directional onlyBenchAlign v5.7 lane · 6 vs 4 public rows | Directional only |
| Instruction following | 91.9#7/124 | 68.5#67/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 65.9#10/19 | 55.9Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 71.4#20/50 | 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 |
| Math | 69.4Unranked · 3 rankable rows | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
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-5.5
Nemotron 3.5 Lightning 30B A3B NVFP4
GPT-5.5
gpt-5.5
OpenAI pricingNemotron 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-5.5
$0.5 per 1M cached input tokens
OpenAI pricingNemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardGPT-5.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
GPT-5.5
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
GPT-5.5
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
GPT-5.5
Proprietary
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
GPT-5.5
2026-04-23
Nemotron 3.5 Lightning 30B A3B NVFP4
2026-08-11
GPT-5.5 has the higher public score, 69.13 versus 19.1, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-5.5 scores higher for coding on the public lane, 64.1 to 11.4. 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.
GPT-5.5 scores higher for agentic tasks on the public lane, 60.1 to 7.3. 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
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
CyberGym
Not directly comparable
BrowseComp
GPT-5.5 leads this result
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
ExploitGym
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
PinchBench
Not directly comparable
τ³-bench results
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
cursorBench31
Not directly comparable
cursorBench32
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SciCode
Not directly comparable
MRCR v2 64K-128K
Not directly comparable
MRCR v2 128K-256K
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
GPQA
GPT-5.5 leads this result
GPQA-D
GPT-5.5 leads this result
HLE
Not directly comparable
HLE w/o tools
GPT-5.5 leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMLU-Pro
Not directly comparable
IFBench
Not directly comparable
FrontierMath (legacy)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated September 24, 2026
