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GPT-5.3-Codex vs Muse Spark

Full profiles: GPT-5.3-Codex (OpenAI) and Muse Spark (Meta): every benchmark, price and rank.

Updated · Data from 8 sources

As of 2 Nov 2026, GPT-5.3-Codex and Muse Spark are neck and neck on quality (62.9 vs 64.3 on the BenchLeader Index, inside the ±7.7 margin). Choose GPT-5.3-Codex for agents & tools (80 vs 66) and long context (67 vs 64); choose Muse Spark for coding (65 vs 61), multimodal (64 vs 61), and instruction following (77 vs 74).

Key facts (as of 2 Nov 2026)

  • GPT-5.3-Codex scores 62.9 on the BenchLeader Index and Muse Spark scores 64.3 (BenchLeader, 2 Nov 2026).
  • GPT-5.3-Codex costs $4.81 per 1M tokens blended (list price, 2 Nov 2026).
  • Of the 11 benchmarks both models have, GPT-5.3-Codex leads 8 (BenchLeader, 2 Nov 2026).
GPT-5.3-Codex benchmarks xhigh OpenAI Muse Spark benchmarks MetaAdd a third model

Which to pick for…

The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.

Use casePickWhyConfidence
CodingMuse Spark65 vs 61 category score; SWE-bench (Vals) 74.4% vs 78.0%Medium
Agents & tool useGPT-5.3-Codex80 vs 66 category score; Terminal-Bench Hard 53.0% vs 45.5%High
Long documentsGPT-5.3-Codex67 vs 64 category score; AA-LCR v1.1 83.3% vs 78.0%Medium
Images and other mediaMuse Spark64 vs 61 category score; MMMU-Pro 80.5% vs 78.5%Medium
Following instructionsMuse Spark77 vs 74 category score; IFBench 75.9% vs 75.4%Medium
Chat quality (human preference)–Only Muse Spark has an Arena rating (68 category score)Not comparable
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • GPT-5.3-Codex (xhigh) and Muse Spark are neck and neck on quality (62.9 vs 64.3, within the ±7.7 margin).
  • GPT-5.3-Codex (xhigh) is stronger in agents & tools, long context.
  • Muse Spark is stronger in coding, instruction following, multimodal, human preference, knowledge, maths, reasoning.

At a glance

SpecGPT-5.3-CodexMuse Spark
DeveloperOpenAIMeta
Released5 Feb 20268 Apr 2026
Context window (tokens)400,000262,144
Compared atGPT-5.3-Codex (xhigh)Muse Spark
Open weightsNoNo
Price in / out / cached input per 1M$1.75 / $14.00 / $0.175–

Category by category

Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.

CategoryGPT-5.3-Codex (xhigh)Muse SparkLeader
Reasoning–67.6not comparable
Coding60.964.9Muse Spark
Agents & tools80.066.1GPT-5.3-Codex
Maths–59.0not comparable
Knowledge–62.9not comparable
Instruction following73.576.6Muse Spark
Human preference–68.1not comparable
Multimodal61.164.2Muse Spark
Long context66.663.8GPT-5.3-Codex

On one chart

Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.

2550 avg75CodingAgents & toolsInstructionfollowingLong contextMultimodalGPT-5.3-Codex, Coding: 60.9GPT-5.3-Codex, Agents & tools: 80.0GPT-5.3-Codex, Instruction following: 73.5GPT-5.3-Codex, Long context: 66.6GPT-5.3-Codex, Multimodal: 61.1Muse Spark, Coding: 64.9Muse Spark, Agents & tools: 66.1Muse Spark, Instruction following: 76.6Muse Spark, Long context: 63.8Muse Spark, Multimodal: 64.2

Not drawn, measured for fewer than two of these models: Reasoning, Maths, Knowledge, Human preference.

ModelIndex

Where sources disagree

Across the 11 benchmarks both have, GPT-5.3-Codex leads 8, Muse Spark leads 3.

Overall indexGPT-5.3-Codex (xhigh)Muse SparkLeader
BenchLeader Index62.964.3neck and neck

Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).

Price, speed and latency

List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.

MeasureGPT-5.3-Codex (xhigh)Muse Spark
Blended price per 1M tokens$4.81–
Input / output per 1M$1.75 / $14.00– / –
Cached input per 1M$0.175–
Price per task: chat reply$0.0049–
Price per task: summarize a 30-page report$0.029–
Price per task: code edit$0.032–
Price per task: agentic coding session$0.161–
Price per task: structured extraction$0.0063–
Agentic coding session, input mostly cached$0.090–
Output speed114 tok/s–
Time to first answer64.7 s–
Time to a complete answer69.1 s–

Each model is compared at its best-ranked reasoning-effort setting. GPT-5.3-Codex (xhigh) scores 62.9 and takes 65 s to its first answer token. Muse Spark scores 64.3. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.

Every benchmark

63 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.

MetricPublisherGPT-5.3-Codex (xhigh)Muse Spark
BenchLeader IndexBenchLeader62.964.3
Agents & tools scoreBenchLeader80.066.1
Coding scoreBenchLeader60.964.9
Instruction following scoreBenchLeader73.576.6
Long context scoreBenchLeader66.663.8
Multimodal scoreBenchLeader61.164.2
Human preference scoreBenchLeader–68.1
Knowledge scoreBenchLeader–62.9
Maths scoreBenchLeader–59.0
Reasoning scoreBenchLeader–67.6
Blended price $/MBenchLeader$4.81–
Output speedBenchLeader114 tok/s–
Time to first answerBenchLeader64.7 s–
Context windowBenchLeader400,000 tokens262,144 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub–89.8%
Humanity's Last ExamScale AI / CAIS–40.6%
Arena Hard PromptsArena (LMArena)–1506
GPQA Diamond (AA)Artificial Analysis91.5%88.4%
Humanity's Last Exam (AA)Artificial Analysis42.5%40.7%
GPQA Diamond (Vals)Vals AI–89.7%
CritPtArtificial Analysis16.9%11.3%
Coding
SWE-bench Verified (Epoch)Epoch AI Benchmarking Hub74.8%†–
SciCodeSciCode–51.5%
WeirdMLWeirdML77.9%–
Arena CodingArena (LMArena)–1529
Arena WebDevArena (LMArena)1407†–
LiveCodeBenchVals AI87.3%–
IOIVals AI53.8%–
SWE-bench (Vals)Vals AI78.0%74.4%
SWE-Bench ProScale AI SEAL–55.0%
Vibe Code Bench v1.1Vals AI61.8%19.7%
SWE-Bench Pro (private)Scale AI SEAL–55.0%
SWE Atlas: Codebase QnAScale AI SEAL–24.2%
SWE Atlas: Test WritingScale AI SEAL–31.1%
ALE-BenchEpoch AI Benchmarking Hub1655.2–
Terminal-Bench 2.1 (AA)Artificial Analysis–62.2%
Agents & tools
Terminal-BenchTerminal-Bench78.4%†–
Terminal-Bench HardArtificial Analysis53.0%45.5%
τ²-Bench Telecom (AA)Artificial Analysis86.0%91.5%
Show all 63 benchmarks
MetricPublisherGPT-5.3-Codex (xhigh)Muse Spark
Agents & tools
MCP AtlasScale AI SEAL–82.2%
HiL-BenchScale AI SEAL4.3%†–
Terminal-Bench 2.0 (Vals)Vals AI64.0%†59.5%
METR Time HorizonsMETR74.5%†–
Vending-Bench 2Andon Labs5940.1†–
AA-Briefcase v1.1Artificial Analysis868–
Maths
OTIS Mock AIMEEpoch AI Benchmarking Hub–88.9%
ProofBenchVals AI–17.0%
AIME (Vals)Vals AI–96.9%
Arena MathsArena (LMArena)–1466
Knowledge
MMLU-ProVals AI–87.3%
LegalBenchVals AI–84.2%
CorpFinVals AI–65.1%
TaxEvalVals AI–77.7%
PRBench FinanceScale AI SEAL–52.4%
PRBench LegalScale AI SEAL–52.3%
MultiNRCScale AI SEAL–59.0%
CaseLaw v2Vals AI–63.1%
MedCodeVals AI–51.3%
MedScribeVals AI–85.9%
Instruction following
IFBenchArtificial Analysis75.4%75.9%
MultiChallengeScale AI SEAL–75.5%
TutorBenchScale AI SEAL–68.5%
Arena Instruction FollowingArena (LMArena)–1464
Human preference
Arena TextArena (LMArena)–1489
Arena Creative WritingArena (LMArena)–1464
Arena Multi-turnArena (LMArena)–1493
Arena Longer QueriesArena (LMArena)–1476
Multimodal
Arena VisionArena (LMArena)–1306
MMMU-ProArtificial Analysis78.5%80.5%
MMMU-Pro (official)MMMU–80.4%†
MMMU-Pro (Vals)Vals AI–87.4%
Long context
AA-LCR v1.1Artificial Analysis83.3%78.0%
Arena DocumentArena (LMArena)–1467
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub–152.2
AA Intelligence Index v4.3.2Artificial Analysis32.531.3
AA Progress Index v1Artificial Analysis301.3289.1
Safety & honesty
FORTRESSScale AI SEAL–20.2%

Data as of 2026-11-02. Best configuration of each model; every score links to its source on the model pages.

What changed

  • GPT-5.3-Codex (xhigh): 1 new result (AA-Briefcase v1.1).
  • GPT-5.3-Codex (xhigh): 1 new result (AA Progress Index v1).
  • Muse Spark: 1 new result (AA Progress Index v1).
  • Muse Spark: 1 new result (PRBench Legal).
  • Muse Spark: 1 new result (SWE Atlas: Codebase QnA).
  • Muse Spark: 2 new results (PRBench Legal, MultiNRC).
  • Muse Spark: BenchLeader Index 65.1 → 64.6.
  • Muse Spark: 1 new result (PRBench Finance).
  • Muse Spark: 1 new result (PRBench Finance).
  • Muse Spark: BenchLeader Index 64.6 → 65.1.

Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs

GPT-5.3-Codex vs Muse Spark: questions

Is GPT-5.3-Codex better than Muse Spark?
GPT-5.3-Codex (xhigh) and Muse Spark are neck and neck on quality (62.9 vs 64.3, within the ±7.7 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2 Nov 2026, so check the category scores for your use.
Is GPT-5.3-Codex or Muse Spark better for coding?
Muse Spark scores higher in coding (65 vs 61 on the category index, where 50 is average).
Is GPT-5.3-Codex or Muse Spark better for agents and tool use?
GPT-5.3-Codex scores higher in agentic tasks (80 vs 66 on the category index, where 50 is average). On Terminal-Bench Hard, the top shared agents & tools benchmark, it is 53.0% for GPT-5.3-Codex against 45.5% for Muse Spark.
Which is better for long documents?
GPT-5.3-Codex scores higher on long-context benchmarks (67 vs 64). GPT-5.3-Codex accepts more context: 400,000 against 262,144 tokens for Muse Spark.
Do independent benchmarks agree on GPT-5.3-Codex vs Muse Spark?
Across the 11 benchmarks both have, GPT-5.3-Codex leads 8, Muse Spark leads 3.
More questions (1)
When was Muse Spark released?
Muse Spark was released on 8 Apr 2026.