GPT-5.3-Codex vs Muse Spark 1.1
Full profiles: GPT-5.3-Codex (OpenAI) and Muse Spark 1.1 (Meta): every benchmark, price and rank.
Updated · Data from 9 sources
As of 2 Nov 2026, GPT-5.3-Codex and Muse Spark 1.1 are neck and neck on quality (62.9 vs 62.2 on the BenchLeader Index, inside the ±8.9 margin), and Muse Spark 1.1 is 2.4× cheaper at $2.00 vs $4.81 per 1M tokens blended. Choose GPT-5.3-Codex for agents & tools (80 vs 52) and instruction following (74 vs 70); choose Muse Spark 1.1 for fast first answers (1.8 s vs 64.7 s) and coding (66 vs 61) and multimodal (64 vs 61), where it also streams ~2× faster.
Key facts (as of 2 Nov 2026)
- GPT-5.3-Codex scores 62.9 on the BenchLeader Index and Muse Spark 1.1 scores 62.2 (BenchLeader, 2 Nov 2026).
- GPT-5.3-Codex costs $4.81 per 1M tokens blended (list price, 2 Nov 2026).
- Muse Spark 1.1 costs $2.00 per 1M tokens blended (list price, 2 Nov 2026).
- Muse Spark 1.1 streams ~2× faster than GPT-5.3-Codex (Artificial Analysis, 7-day median, 2 Nov 2026).
GPT-5.3-Codex benchmarks 
Muse Spark 1.1 benchmarks 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 case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Muse Spark 1.1 | 66 vs 61 category score | Medium |
| Agents & tool use | GPT-5.3-Codex | 80 vs 52 category score | High |
| Images and other media | Muse Spark 1.1 | 64 vs 61 category score | Medium |
| Following instructions | GPT-5.3-Codex | 74 vs 70 category score | Medium |
| Chat quality (human preference) | – | Only Muse Spark 1.1 has an Arena rating (65 category score) | Not comparable |
| Lowest cost | Muse Spark 1.1 | $2.00 vs $4.81 per 1M blended; agentic coding session $0.092 vs $0.161 | – |
| Fastest first answer | Muse Spark 1.1 | 1.8 s (Muse Spark 1.1) vs 64.7 s (GPT-5.3-Codex (xhigh)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-5.3-Codex (xhigh) and Muse Spark 1.1 are neck and neck on quality (62.9 vs 62.2, within the ±8.9 margin).
- GPT-5.3-Codex (xhigh) is stronger in agents & tools, instruction following, long context.
- Muse Spark 1.1 is stronger in coding, multimodal, human preference, knowledge, maths, reasoning.
- Muse Spark 1.1 is 2.4× cheaper ($2.00 vs $4.81 per 1M blended).
- Muse Spark 1.1 streams 1.8× faster (206 vs 114 tokens per second).
At a glance
| Spec | GPT-5.3-Codex | Muse Spark 1.1 |
|---|---|---|
| Developer | OpenAI | Meta |
| Released | 5 Feb 2026 | 9 Jul 2026 |
| Context window (tokens) | 400,000 | 1,048,576 |
| Compared at | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 |
| Open weights | No | No |
| Price in / out / cached input per 1M | $1.75 / $14.00 / $0.175 | $1.25 / $4.25 / $0.150 |
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.
| Category | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Reasoning | – | 67.8 | not comparable |
| Coding | 60.9 | 66.0 | Muse Spark 1.1 |
| Agents & tools | 80.0 | 51.5 | GPT-5.3-Codex |
| Maths | – | 61.2 | not comparable |
| Knowledge | – | 69.2 | not comparable |
| Instruction following | 73.5 | 69.9 | GPT-5.3-Codex |
| Human preference | – | 64.5 | not comparable |
| Multimodal | 61.1 | 63.6 | Muse Spark 1.1 |
| Long context | 66.6 | – | not comparable |
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.
Not drawn, measured for fewer than two of these models: Reasoning, Maths, Knowledge, Long context, Human preference.
Where sources disagree
Across the 0 benchmarks both have, GPT-5.3-Codex leads 0, Muse Spark 1.1 leads 0.
| Overall index | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 | Leader |
|---|---|---|---|
| BenchLeader Index | 62.9 | 62.2 | neck 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.
| Measure | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 |
|---|---|---|
| Blended price per 1M tokens | $4.81 | $2.00 |
| Input / output per 1M | $1.75 / $14.00 | $1.25 / $4.25 |
| Cached input per 1M | $0.175 | $0.150 |
| Price per task: chat reply | $0.0049 | $0.0018 |
| Price per task: summarize a 30-page report | $0.029 | $0.018 |
| Price per task: code edit | $0.032 | $0.014 |
| Price per task: agentic coding session | $0.161 | $0.092 |
| Price per task: structured extraction | $0.0063 | $0.0034 |
| Agentic coding session, input mostly cached | $0.090 | $0.043 |
| Output speed | 114 tok/s | 206 tok/s |
| Time to first answer | 64.7 s | 1.8 s |
| Time to a complete answer | 69.1 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 1.1 scores 62.2 and takes 2 s to its first answer token; Muse Spark 1.1 (xhigh) scores 59.5 in 18 s. 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
93 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.
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 62.9 | 62.2 |
| Agents & tools score | BenchLeader | 80.0 | 51.5 |
| Coding score | BenchLeader | 60.9 | 66.0 |
| Instruction following score | BenchLeader | 73.5 | 69.9 |
| Long context score | BenchLeader | 66.6 | – |
| Multimodal score | BenchLeader | 61.1 | 63.6 |
| Human preference score | BenchLeader | – | 64.5 |
| Knowledge score | BenchLeader | – | 69.2 |
| Maths score | BenchLeader | – | 61.2 |
| Reasoning score | BenchLeader | – | 67.8 |
| Blended price $/M | BenchLeader | $4.81 | $2.00 |
| Output speed | BenchLeader | 114 tok/s | 206 tok/s |
| Time to first answer | BenchLeader | 64.7 s | 1.8 s |
| Context window | BenchLeader | 400,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| Arena Hard Prompts | Arena (LMArena) | – | 1511 |
| LiveBench 1.0 Reasoning | LiveBench | – | 87.7%† |
| GPQA Diamond (AA) | Artificial Analysis | 91.5% | 89.8%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.5% | 46.2%† |
| GPQA Diamond (Vals) | Vals AI | – | 91.2%† |
| CritPt | Artificial Analysis | 16.9% | 15.1%† |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 34.0%† |
| LMCA | Epoch AI Benchmarking Hub | – | 49.9%† |
| DTBench | Epoch AI Benchmarking Hub | – | 94.4%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 74.8%† | – |
| SciCode | SciCode | – | 58.8% |
| WeirdML | WeirdML | 77.9% | – |
| Arena Coding | Arena (LMArena) | – | 1533 |
| Arena WebDev | Arena (LMArena) | 1407† | 1543 |
| LiveBench 1.0 Coding | LiveBench | – | 77.2%† |
| LiveCodeBench | Vals AI | 87.3% | 85.9%† |
| IOI | Vals AI | 53.8% | – |
| SWE-bench (Vals) | Vals AI | 78.0% | 82.0%† |
| SWE-Bench Pro | Scale AI SEAL | – | 61.5% |
| Code Migration | Vals AI | – | 31.1%† |
| ProgramBench | Vals AI | – | 0.0%† |
| Vibe Code Bench v1.1 | Vals AI | 61.8% | 72.2%† |
| SWE-Bench Pro (private) | Scale AI SEAL | – | 61.5% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 42.2%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 41.5%† |
Show all 93 benchmarks
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Muse Spark 1.1 |
|---|---|---|---|
| Coding | |||
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 52.5%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 53.3% |
| ALE-Bench | Epoch AI Benchmarking Hub | 1655.2 | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 6.1%† |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 77.9%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 78.4%† | – |
| APEX-Agents | Mercor | – | 31.8% |
| Arena Agent | Arena (LMArena) | – | -7.7 |
| LiveBench Agentic Coding | LiveBench | – | 58.5%† |
| Terminal-Bench Hard | Artificial Analysis | 53.0% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 86.0% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 69.3%† |
| MCP Atlas | Scale AI SEAL | – | 88.1% |
| HiL-Bench | Scale AI SEAL | 4.3%† | – |
| τ³-bench Banking | τ²-bench | – | 40.5%† |
| τ³-Banking (AA) | Artificial Analysis | – | 31.8%† |
| Finance Agent v2 | Vals AI | – | 57.2%† |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 20.0%† |
| Legal Research Bench | Vals AI | – | 38.0%† |
| SkillsBench | Vals AI | – | 59.2%† |
| Terminal-Bench 2.0 (Vals) | Vals AI | 64.0%† | – |
| Time Horizon Index: KSP | Vals AI | – | 1.7%† |
| METR Time Horizons | METR | 74.5%† | – |
| GBAEval | Epoch AI Benchmarking Hub | – | 7.9% |
| Vending-Bench 2 | Andon Labs | 5940.1† | 6520.5 |
| AutomationBench | Artificial Analysis | – | 38.9%† |
| GDP.pdf (AA) | Artificial Analysis | – | 14.4%† |
| AA-Briefcase v1.1 | Artificial Analysis | 868 | – |
| Maths | |||
| ProofBench | Vals AI | – | 39.0% |
| LiveBench Mathematics | LiveBench | – | 87.1%† |
| Arena Maths | Arena (LMArena) | – | 1494 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 57.8% |
| LiveBench 1.0 Data Analysis | LiveBench | – | 72.5%† |
| MMLU-Pro | Vals AI | – | 88.7%† |
| LegalBench | Vals AI | – | 85.0%† |
| CorpFin | Vals AI | – | 71.3%† |
| TaxEval | Vals AI | – | 79.7%† |
| PRBench Finance | Scale AI SEAL | – | 55.0% |
| PRBench Legal | Scale AI SEAL | – | 57.0% |
| MultiNRC | Scale AI SEAL | – | 65.6% |
| Excel Modeling Benchmark | Vals AI | – | 56.4%† |
| MedScribe | Vals AI | – | 88.9%† |
| Public Benefits Bench v1.1 | Vals AI | – | 61.3%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 15.0% |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 74.3%† |
| LiveBench Instruction Following | LiveBench | – | 69.6%† |
| IFBench | Artificial Analysis | 75.4% | – |
| MultiChallenge | Scale AI SEAL | – | 75.3% |
| Arena Instruction Following | Arena (LMArena) | – | 1472 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1490 |
| EQ-Bench 4 | EQ-Bench | – | 1244 |
| Arena Creative Writing | Arena (LMArena) | – | 1444 |
| Arena Multi-turn | Arena (LMArena) | – | 1496 |
| Arena Longer Queries | Arena (LMArena) | – | 1480 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1293 |
| MMMU-Pro | Artificial Analysis | 78.5% | – |
| MMMU-Pro (Vals) | Vals AI | – | 86.6%† |
| MortgageTax | Vals AI | – | 66.1%† |
| VTB | Scale AI SEAL | – | 44.8% |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 77.7%† |
| Arena Document | Arena (LMArena) | – | 1480 |
| MLCR | Artificial Analysis | – | 7.2%† |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 154.2 |
| LiveBench 1.0 | LiveBench | – | 75.3%† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 32.5 | 33.7† |
| AA Progress Index v1 | Artificial Analysis | 301.3 | 302.7† |
| Vals Multimodal Index | Vals AI | – | 66.7%† |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 12.4% |
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).
- Muse Spark 1.1: 2 score revisions.
- GPT-5.3-Codex (xhigh): 1 new result (AA Progress Index v1).
- Muse Spark 1.1: 1 score revision.
- Muse Spark 1.1: BenchLeader Index 63.1 → 62.5.
- Muse Spark 1.1: 1 score revision.
- Muse Spark 1.1: 1 new result (PRBench Legal).
- Muse Spark 1.1: 1 new result (VTB).
- Muse Spark 1.1: BenchLeader Index 62.5 → 63.1.
- Muse Spark 1.1: 2 new results (PRBench Legal, MultiNRC).
Related comparisons
- GPT-5.3-Codex vs Claude Opus 5.5
- GPT-5.3-Codex vs Gemini 4 Carbon
- GPT-5.3-Codex vs GPT-6 Astra
- Muse Spark 1.1 vs Claude Opus 5.5
- Muse Spark 1.1 vs Gemini 4 Carbon
- Muse Spark 1.1 vs GPT-6 Astra
- Muse Spark 1.1 vs GPT-6.1 Sol
- GPT-5.3-Codex vs Muse Spark
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GPT-5.3-Codex vs Muse Spark 1.1: questions
- Is GPT-5.3-Codex better than Muse Spark 1.1?
- GPT-5.3-Codex (xhigh) and Muse Spark 1.1 are neck and neck on quality (62.9 vs 62.2, within the ±8.9 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.
- GPT-5.3-Codex vs Muse Spark 1.1: which is cheaper, and will that change?
- Muse Spark 1.1 is cheaper today: $2.00 against $4.81 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is GPT-5.3-Codex or Muse Spark 1.1 better for coding?
- Muse Spark 1.1 scores higher in coding (66 vs 61 on the category index, where 50 is average).
- Which is faster, GPT-5.3-Codex or Muse Spark 1.1?
- Muse Spark 1.1 streams ~2× faster (206 vs 114 output tokens per second, Artificial Analysis 7-day median).
- Is GPT-5.3-Codex or Muse Spark 1.1 better for agents and tool use?
- GPT-5.3-Codex scores higher in agentic tasks (80 vs 52 on the category index, where 50 is average).
More questions (3)
- Which is better for long documents?
- Muse Spark 1.1 accepts more context: 1,048,576 against 400,000 tokens for GPT-5.3-Codex.
- Which has lower latency?
- Muse Spark 1.1 answers first: 1.8 s against 64.7 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- When was Muse Spark 1.1 released?
- Muse Spark 1.1 was released on 9 Jul 2026.