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GPT-5.3-Codex vs Nemotron 3.5 Super VL

Updated October 21, 2026. 8 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-5.3-Codex Replace Nemotron 3.5 Super VL
GPT-5.3-CodexOpenAI
Coding
29.5
Overall
32.5
Output $/M
$14.00
4
Nemotron 3.5 Super VLNVIDIA
Coding
65.5
Overall
26.5
Output $/M
$0.50
4
vs boards won of 8 shared

Decision reading

GPT-5.3-Codex scores 29.5 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.

Pick GPT-5.3-Codex when what matters is AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads; pick Nemotron 3.5 Super VL when output tokens cost 28.0x less, or when the context window is larger, 1.05M against 400K tokens.

Model AGPT-5.3-CodexOpenAI29.5/1001 independent benchmark$0.59 per solved taskModel BNemotron 3.5 Super VLNVIDIA65.5/1001 independent benchmarkNo cost per solved task
Shared results
8
GPT-5.3-Codex only
26
Nemotron 3.5 Super VL only
8
Like-for-like categories
4 of 9

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

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5.3-Codex 29.5 from 1 benchmark, Nemotron 3.5 Super VL 65.5 from 1 benchmark.
Widest category
General, 14.3 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: 3D: GPT-5.3-Codex 1008 against Nemotron 3.5 Super VL 1132, 15.5 points apart on a 0 to 100 scale.
Output price
GPT-5.3-Codex $14.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 28.0x apart.
Context window
GPT-5.3-Codex takes 400K tokens, Nemotron 3.5 Super VL 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-5.3-CodexBarsNemotron 3.5 Super VLTies
Intelligence10
Reasoning10
Web design14
General10
All shared boards44
CodingDirectional onlyFour of these boards feed the Vibe Coding Index14 boards, none shared
Coding benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
LiveBench codingNot measured65.5%Only Nemotron 3.5 Super VL measured
LMArena WebDev1407Not measuredOnly GPT-5.3-Codex measured
LiveCodeBench (Vals AI)87.3%Not measuredOnly GPT-5.3-Codex measured
SWE-bench Verified (Vals AI)78.0%Not measuredOnly GPT-5.3-Codex measured
Vibe Code Bench (Vals AI)61.8%Not measuredOnly GPT-5.3-Codex measured
IOI (Vals AI)53.8%Not measuredOnly GPT-5.3-Codex measured
SciCodeNot measured47.8%Only Nemotron 3.5 Super VL measured
Terminal-Bench Hard (Artificial Analysis run)53.0%Not measuredOnly GPT-5.3-Codex measured
Terminal-Bench78.4%Not measuredOnly GPT-5.3-Codex measured
WeirdML79.3%Not measuredOnly GPT-5.3-Codex measured
ALE-Bench1655.2Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: React1395Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: HTML1422Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: from an image1443Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
AA Intelligence Index32.526.5GPT-5.3-Codex
Epoch Capabilities Index157.1Not measuredOnly GPT-5.3-Codex measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
Design Arena: agents building web apps1033Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building full-stack apps983Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building mobile apps1054Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Android apps1084Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Godot games1124Not measuredOnly GPT-5.3-Codex measured
LiveBench researchNot measured70.9%Only Nemotron 3.5 Super VL measured
LiveBench browserNot measured4.0%Only Nemotron 3.5 Super VL measured
LiveBench long agenticNot measured15.8%Only Nemotron 3.5 Super VL measured
τ²-Bench Telecom86.0%Not measuredOnly GPT-5.3-Codex measured
METR time horizons74.5%Not measuredOnly GPT-5.3-Codex measured
Vending-Bench 2$5,940Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
Reasoning benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
LiveBench reasoningNot measured78.8%Only Nemotron 3.5 Super VL measured
GPQA Diamond (Artificial Analysis run)91.5%Not measuredOnly GPT-5.3-Codex measured
Humanity's Last Exam (Artificial Analysis run)42.5%30.7%GPT-5.3-Codex
Neither model has a result on the other 18 boards here
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Math benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
Neither model has a result on 13 boards here
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Knowledge benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
Neither model has a result on 17 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index14
Web design benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
Design Arena: websites11631171Nemotron 3.5 Super VL
Design Arena: UI components11371148Nemotron 3.5 Super VL
Design Arena: game dev11621108GPT-5.3-Codex
Design Arena: data viz1164Not measuredOnly GPT-5.3-Codex measured
Design Arena: 3D10081132Nemotron 3.5 Super VL
Design Arena: all code categories11451155Nemotron 3.5 Super VL
Design Arena: SVG1129Not measuredOnly GPT-5.3-Codex measured
Design Arena: ASCII art1151Not measuredOnly GPT-5.3-Codex measured
LiveBench designNot measured60.7%Only Nemotron 3.5 Super VL measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
General benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
LiveBench data analysisNot measured64.6%Only Nemotron 3.5 Super VL measured
IFBench75.4%Not measuredOnly GPT-5.3-Codex measured
Long context reasoning (AA-LCR)83.3%69.0%GPT-5.3-Codex
Neither model has a result on the other 43 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.3-Codex and Nemotron 3.5 Super VL
BenchmarkGPT-5.3-CodexNemotron 3.5 Super VLLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.3-Codex$0.0088Fits in one request
Nemotron 3.5 Super VL$0.0004Fits in one request

Nemotron 3.5 Super VL costs less

Repository review

50K input + 3K output tokens

GPT-5.3-Codex$0.130Fits in one request
Nemotron 3.5 Super VL$0.0090Fits in one request

Nemotron 3.5 Super VL costs less

Agent loop

200K input + 10K output tokens

GPT-5.3-Codex$0.490Fits in one request
Nemotron 3.5 Super VL$0.035Fits in one request

Nemotron 3.5 Super VL costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.3-Codex and Nemotron 3.5 Super VL
SpecificationOpenAI GPT-5.3-CodexNVIDIA Nemotron 3.5 Super VL
MakerOpenAINVIDIA
WeightsNo public weights listedOpen weights
ReleasedFebruary 24, 2026October 15, 2026
Context window400K1.05M
Input $/Mtok$1.75$0.15
Output $/Mtok$14.00$0.50
Coding index29.565.5
Independent measurements behind it11
Cost per solved task$0.59Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.3-Codex better than Nemotron 3.5 Super VL for coding?

GPT-5.3-Codex scores 29.5 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick GPT-5.3-Codex when what matters is AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads; pick Nemotron 3.5 Super VL when output tokens cost 28.0x less, or when the context window is larger, 1.05M against 400K tokens.

Which is cheaper, GPT-5.3-Codex or Nemotron 3.5 Super VL?

GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for GPT-5.3-Codex only.

Which has the bigger context window, GPT-5.3-Codex or Nemotron 3.5 Super VL?

Nemotron 3.5 Super VL, with 1.05M tokens against 400K.

How many benchmarks is the GPT-5.3-Codex vs Nemotron 3.5 Super VL comparison based on?

8 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 1 independent benchmark and Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.

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