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BenchLM

GPT-6 Sol vs Nemotron 3.5 Lightning 30B A3B NVFP4

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

OpenAI logo
Model A
GPT-6 Sol

OpenAI

80.45/100

Estimated · Public rank #7

90% interval 51.1–92.0

NVIDIA logo
Model B
Nemotron 3.5 Lightning 30B A3B NVFP4

NVIDIA

21.95/100

Estimated · Public rank #246

90% interval 12.1–31.8

Updated September 22, 2026. Rank says GPT-6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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

    GPT-6 Sol

    GPT-6 Sol 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

    GPT-6 Sol and Nemotron 3.5 Lightning 30B A3B NVFP4 are 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

    GPT-6 Sol and Nemotron 3.5 Lightning 30B A3B NVFP4 are 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: 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

Which one for a specific job

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.

74.3GPT-6 Sol31.1Nemotron 3.5 Lightning 30B A3B NVFP4

Directional only · BenchAlign

GPT-6 Sol 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.

Open the coding leaderboardShortlist models for this job

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
GPT-6 Sol only
10
Nemotron 3.5 Lightning 30B A3B NVFP4 only
13
Like-for-like categories
0 / 8

3 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.

Agentic

Directional only
GPT-6 Sol
69.7
Estimated · #7/157
Nemotron 3.5 Lightning 30B A3B NVFP4
27.1
Estimated · #151/157
Basis
BenchAlign lane · 4 vs 4 public rows
Reading
Directional only

Coding

Directional only
GPT-6 Sol
74.3
Estimated · #6/159
Nemotron 3.5 Lightning 30B A3B NVFP4
31.1
Estimated · #145/159
Basis
BenchAlign lane · 1 vs 4 public rows
Reading
Directional only

Knowledge

Directional only
GPT-6 Sol
80.2
Estimated · #6/189
Nemotron 3.5 Lightning 30B A3B NVFP4
31.2
Estimated · #182/189
Basis
BenchAlign lane · 5 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
GPT-6 Sol
78.5
#5/17
Nemotron 3.5 Lightning 30B A3B NVFP4
54.8
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Sol
82.8
Unranked · 1 rankable row
Nemotron 3.5 Lightning 30B A3B NVFP4
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Sol
Not ranked
Nemotron 3.5 Lightning 30B A3B NVFP4
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Sol
Not ranked
Nemotron 3.5 Lightning 30B A3B NVFP4
68.5
#67/124
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-6 Sol
Not ranked
Nemotron 3.5 Lightning 30B A3B NVFP4
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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-6 Sol
$0.007
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-6 Sol
$0.13
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-6 Sol
$0.18
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.

Provider availability

GPT-6 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6 Sol and Luna launch

Nemotron 3.5 Lightning 30B A3B NVFP4

Not sourced

Reasoning profile

GPT-6 Sol

Reasoning

Nemotron 3.5 Lightning 30B A3B NVFP4

Reasoning

Weight access

GPT-6 Sol

Proprietary

Nemotron 3.5 Lightning 30B A3B NVFP4

Open Weight

License

GPT-6 Sol

Proprietary

Nemotron 3.5 Lightning 30B A3B NVFP4

Open Weight

Release date

GPT-6 Sol

2026-09-16

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-6 Sol 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 evidence23 rows

Agentic

  • Agents' Last Exam

    GPT-6 Sol56.4%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • AutomationBench

    GPT-6 Sol33.2%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • OSWorld 2.0

    GPT-6 Sol60.5%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • ExploitGym

    GPT-6 Sol22.1%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP423.5%
    Source

    Not directly comparable

  • PinchBench

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP483.4%
    Source

    Not directly comparable

  • BrowseComp

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP436.8%
    Source

    Not directly comparable

  • τ³-bench results

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP49.5%
    Source

    Not directly comparable

Coding

  • DeepSWE

    GPT-6 Sol68.8%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • SWE-bench Verified

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP452.8%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP436.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP423.5%
    Source

    Not directly comparable

  • SciCode

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP431.4%
    Source

    Not directly comparable

Knowledge

  • HealthBench (raw)

    GPT-6 Sol47.1%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • HealthBench (length-adjusted)

    GPT-6 Sol53.2%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • HealthBench Professional

    GPT-6 Sol60.8%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • HealthBench Professional (raw)

    GPT-6 Sol59.5%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • HealthBench Hard

    GPT-6 Sol30.1%
    Source
    Nemotron 3.5 Lightning 30B A3B NVFP4—

    Not directly comparable

  • GPQA

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP475.6%
    Source

    Not directly comparable

  • GPQA-D

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP475.6%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP410.5%
    Source

    Not directly comparable

  • MMLU-Pro

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP481.6%
    Source

    Not directly comparable

Instruction following

  • IFBench

    GPT-6 Sol—
    Nemotron 3.5 Lightning 30B A3B NVFP472.9%
    Source

    Not directly comparable

Questions

Which is better, GPT-6 Sol or Nemotron 3.5 Lightning 30B A3B NVFP4?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-6 Sol or Nemotron 3.5 Lightning 30B A3B NVFP4?

GPT-6 Sol scores higher for coding on the public lane, 74.3 to 31.1. GPT-6 Sol and Nemotron 3.5 Lightning 30B A3B NVFP4 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.

Which is better for agentic tasks, GPT-6 Sol or Nemotron 3.5 Lightning 30B A3B NVFP4?

GPT-6 Sol scores higher for agentic tasks on the public lane, 69.7 to 27.1. GPT-6 Sol and Nemotron 3.5 Lightning 30B A3B NVFP4 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.

Which costs less, GPT-6 Sol 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-6 Sol or Nemotron 3.5 Lightning 30B A3B NVFP4?

GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 22, 2026

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