GPT-5.3-Codex vs Muse Spark 1.3
Full profiles: GPT-5.3-Codex (OpenAI) and Muse Spark 1.3 (Meta): every benchmark, price and rank.
Updated · Data from 7 sources
As of 2 Nov 2026, GPT-5.3-Codex and Muse Spark 1.3 are neck and neck on quality (62.9 vs 61.9 on the BenchLeader Index, inside the ±7.7 margin), and Muse Spark 1.3 is 2.4× cheaper at $2.00 vs $4.81 per 1M tokens blended. Choose GPT-5.3-Codex for agents & tools (80 vs 66); choose Muse Spark 1.3 for multimodal (66 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 1.3 scores 61.9 (BenchLeader, 2 Nov 2026).
- GPT-5.3-Codex costs $4.81 per 1M tokens blended (list price, 2 Nov 2026).
- Muse Spark 1.3 costs $2.00 per 1M tokens blended (list price, 2 Nov 2026).
- Muse Spark 1.3 streams ~2× faster than GPT-5.3-Codex (Artificial Analysis, 7-day median, 2 Nov 2026).
- Of the 8 benchmarks both models have, Muse Spark 1.3 leads 7 (BenchLeader, 2 Nov 2026).
GPT-5.3-Codex benchmarks 
Muse Spark 1.3 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 | Either | 61 vs 61 category score; IOI 53.8% vs 56.6% | Either |
| Agents & tool use | GPT-5.3-Codex | 80 vs 66 category score; AA-Briefcase v1.1 868 vs 1581 | High |
| Long documents | Either | 67 vs 66 category score; AA-LCR v1.1 83.3% vs 83.0% | Either |
| Images and other media | Muse Spark 1.3 | 66 vs 61 category score | Medium |
| Chat quality (human preference) | – | Only Muse Spark 1.3 has an Arena rating (69 category score) | Not comparable |
| Lowest cost | Muse Spark 1.3 | $2.00 vs $4.81 per 1M blended; agentic coding session $0.092 vs $0.161 | – |
| Fastest first answer | Muse Spark 1.3 | 46.3 s (Muse Spark 1.3 (max)) 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 1.3 (max) are neck and neck on quality (62.9 vs 61.9, within the ±7.7 margin).
- GPT-5.3-Codex (xhigh) is stronger in agents & tools, coding, instruction following, long context.
- Muse Spark 1.3 (max) is stronger in multimodal, human preference, knowledge, maths, reasoning.
- Muse Spark 1.3 (max) is 2.4× cheaper ($2.00 vs $4.81 per 1M blended).
- Muse Spark 1.3 (max) streams 1.7× faster (194 vs 114 tokens per second).
At a glance
| Spec | GPT-5.3-Codex | Muse Spark 1.3 |
|---|---|---|
| Developer | OpenAI | Meta |
| Released | 5 Feb 2026 | 2 Sep 2026 |
| Context window (tokens) | 400,000 | 1,048,576 |
| Compared at | GPT-5.3-Codex (xhigh) | Muse Spark 1.3 (max) |
| 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 1.3 (max) | Leader |
|---|---|---|---|
| Reasoning | – | 60.6 | not comparable |
| Coding | 60.9 | 60.8 | level |
| Agents & tools | 80.0 | 65.5 | GPT-5.3-Codex |
| Maths | – | 63.1 | not comparable |
| Knowledge | – | 58.5 | not comparable |
| Instruction following | 73.5 | – | not comparable |
| Human preference | – | 68.8 | not comparable |
| Multimodal | 61.1 | 65.7 | Muse Spark 1.3 |
| Long context | 66.6 | 66.4 | level |
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, Instruction following, Human preference.
Where sources disagree
Across the 8 benchmarks both have, GPT-5.3-Codex leads 1, Muse Spark 1.3 leads 7.
| Overall index | GPT-5.3-Codex (xhigh) | Muse Spark 1.3 (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 62.9 | 61.9 | 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 1.3 (max) |
|---|---|---|
| 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 |
| Cost per benchmark task (AA Index) | – | $2.39 |
| Output tokens per task (AA Index) | – | 91k |
| Of which reasoning | – | 55k |
| Time per task (AA Index) | – | 8 min |
| Output speed | 114 tok/s | 194 tok/s |
| Time to first answer | 64.7 s | 46.3 s |
| Time to a complete answer | 69.1 s | 48.9 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 1.3 (max) scores 61.9 and takes 46 s to its first answer token; Muse Spark 1.3 (xhigh) scores 58.5 in 22 s, Muse Spark 1.3 scores 57.8 in 2 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
109 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 1.3 (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 62.9 | 61.9 |
| Agents & tools score | BenchLeader | 80.0 | 65.5 |
| Coding score | BenchLeader | 60.9 | 60.8 |
| Instruction following score | BenchLeader | 73.5 | – |
| Long context score | BenchLeader | 66.6 | 66.4 |
| Multimodal score | BenchLeader | 61.1 | 65.7 |
| Human preference score | BenchLeader | – | 68.8 |
| Knowledge score | BenchLeader | – | 58.5 |
| Maths score | BenchLeader | – | 63.1 |
| Reasoning score | BenchLeader | – | 60.6 |
| Blended price $/M | BenchLeader | $4.81 | $2.00 |
| Output speed | BenchLeader | 114 tok/s | 194 tok/s |
| Time to first answer | BenchLeader | 64.7 s | 46.3 s |
| Context window | BenchLeader | 400,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| Arena Hard Prompts | Arena (LMArena) | – | 1516 |
| LiveBench 2.0 Reasoning | LiveBench | – | 89.7%† |
| LiveBench 1.0 Reasoning | LiveBench | – | 89.7%† |
| GPQA Diamond (AA) | Artificial Analysis | 91.5% | 93.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.5% | 48.7% |
| HLE-Diamond (AA) | Artificial Analysis | – | 33.7% |
| CritPt | Artificial Analysis | 16.9% | 24.9% |
| CritPt v2 hard subset (AA) | Artificial Analysis | – | 46.0% |
| MysteryMechanism | Vals AI | – | 36.0% |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 37.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 25.0% |
| LMCA | Epoch AI Benchmarking Hub | – | 53.9%† |
| DTBench | Epoch AI Benchmarking Hub | – | 96.5%† |
| BTF-3 | Epoch AI Benchmarking Hub | – | 14.1%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 74.8%† | – |
| SciCode | SciCode | – | 58.8% |
| WeirdML | WeirdML | 77.9% | – |
| Arena Coding | Arena (LMArena) | – | 1534 |
| Arena WebDev | Arena (LMArena) | 1407† | 1656 |
| LiveBench 2.0 Coding | LiveBench | – | 90.2%† |
| LiveBench 1.0 Coding | LiveBench | – | 81.1%† |
| LiveCodeBench | Vals AI | 87.3% | – |
| IOI | Vals AI | 53.8% | 56.6% |
| SWE-bench (Vals) | Vals AI | 78.0% | – |
| Code Migration | Vals AI | – | 47.4% |
Show all 109 benchmarks
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Muse Spark 1.3 (max) |
|---|---|---|---|
| Coding | |||
| ProgramBench | Vals AI | – | 2.5% |
| SWE-bench Multimodal | Vals AI | – | 43.1% |
| Vibe Code Bench 1-100 | Vals AI | – | 20.5% |
| Vibe Code Bench v1.1 | Vals AI | 61.8% | 85.9% |
| CursorBench | Cursor | – | 41.6% |
| ALE-Bench | Epoch AI Benchmarking Hub | 1655.2 | 1688.3† |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 25.8% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 33.3% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 84.3% |
| Startrise Frontend | Startrise | – | 74.7%† |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 59.7† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 70.9† |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 40.6% |
| FrontierCode 2.0 (AA) | Artificial Analysis | – | 23.1% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 78.4%† | 14.6%† |
| APEX-Agents | Mercor | – | 47.6% |
| Arena Agent | Arena (LMArena) | – | 2.6 |
| LiveBench Long Agentic | LiveBench | – | 25.5%† |
| LiveBench Browser | LiveBench | – | 30.9%† |
| LiveBench Agentic Coding | LiveBench | – | 64.1%† |
| Terminal-Bench Hard | Artificial Analysis | 53.0% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 86.0% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 79.0% |
| HiL-Bench | Scale AI SEAL | 4.3%† | 66.9%† |
| τ³-Banking (AA) | Artificial Analysis | – | 50.5% |
| ITBench SRE (AA) | Artificial Analysis | – | 33.2% |
| Analyst Agent (AA) | Artificial Analysis | – | 37.5% |
| CUA-bench | Vals AI | – | 5.8% |
| CyberBench | Vals AI | – | 72.7% |
| Finance Agent v2 | Vals AI | – | 60.0% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 23.8% |
| Legal Research Bench | Vals AI | – | 55.3% |
| SREBench | Vals AI | – | 3.8% |
| Tax Agent Bench | Vals AI | – | 42.4% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 64.0%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 24.8% |
| Terminal-Bench Science | Vals AI | – | 10.0% |
| METR Time Horizons | METR | 74.5%† | – |
| Vending-Bench 2 | Andon Labs | 5940.1† | – |
| AutomationBench | Artificial Analysis | – | 57.9% |
| GDP.pdf (AA) | Artificial Analysis | – | 26.6% |
| Harvey LAB-AA v1.1 | Harvey | – | 8.9% |
| AA-Briefcase v1.1 | Artificial Analysis | 868 | 1581 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 10.9% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 74.0% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 46.3% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 99.2%† |
| ProofBench | Vals AI | – | 58.0% |
| LiveBench Mathematics | LiveBench | – | 96.0%† |
| Arena Maths | Arena (LMArena) | – | 1508 |
| Knowledge | |||
| LiveBench 2.0 Data Analysis | LiveBench | – | 79.6%† |
| LiveBench Research | LiveBench | – | 68.6%† |
| LiveBench 1.0 Data Analysis | LiveBench | – | 79.6%† |
| AA-Omniscience v1.1 | Artificial Analysis | – | 22.4 |
| PRBench Finance | Scale AI SEAL | – | 59.5%† |
| PRBench Legal | Scale AI SEAL | – | 61.6%† |
| Excel Modeling Benchmark | Vals AI | – | 67.4% |
| DrugDiscoveryBench | Scale AI SEAL | – | 62.2%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 27.6%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 38.0% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 74.8% |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 82.8%† |
| LiveBench Instruction Following | LiveBench | – | 78.0%† |
| IFBench | Artificial Analysis | 75.4% | – |
| Arena Instruction Following | Arena (LMArena) | – | 1483 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1495 |
| Arena Creative Writing | Arena (LMArena) | – | 1464 |
| Arena Multi-turn | Arena (LMArena) | – | 1493 |
| Arena Longer Queries | Arena (LMArena) | – | 1496 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1308 |
| LiveBench Design | LiveBench | – | 74.8%† |
| MMMU-Pro | Artificial Analysis | 78.5% | 82.0%† |
| Code2Video-AA | Artificial Analysis | – | 1019 |
| AA-3D | Artificial Analysis | – | 1033 |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 83.0% |
| Arena Document | Arena (LMArena) | – | 1468 |
| MLCR | Artificial Analysis | – | 13.9% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 157.4† |
| LiveBench 2.0 | LiveBench | – | 63.0%† |
| LiveBench 1.0 | LiveBench | – | 81.6%† |
| AA Intelligence Index v5.0 | Artificial Analysis | – | 36.4 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 32.5 | 48.1 |
| AA Progress Index v1 | Artificial Analysis | 301.3 | 324.5 |
| Vals RSI Index | Vals AI | – | 19.6% |
Data as of 2026-11-02. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261026-cb69502a12d9 (https://benchmarks.duellab.org/releases/gb2-20261026-cb69502a12d9/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- GPT-5.3-Codex (xhigh): 1 new result (AA-Briefcase v1.1).
- Muse Spark 1.3 (max): 1 score revision.
- Muse Spark 1.3 (max): BenchLeader Index 64.5 → 61.8.
- GPT-5.3-Codex (xhigh): 1 new result (AA Progress Index v1).
- Muse Spark 1.3 (max): 5 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 3 score revisions; cost per task moved from $1.31/M to $2.39/M (81% up).
- Muse Spark 1.3 (max): cost per task moved from $3.69/M to $1.31/M (64% down).
- Muse Spark 1.3 (max): cost per task moved from $1.60/M to $3.69/M (130% up).
- Muse Spark 1.3 (max): 3 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index, SWE-bench Multimodal); 1 score revision.
- Muse Spark 1.3 (max): 1 score revision.
- Muse Spark 1.3 (max): 1 score revision.
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- Muse Spark 1.3 vs Gemini 4 Carbon
- Muse Spark 1.3 vs GPT-6 Astra
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- GPT-5.2 vs Muse Spark 1.3
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GPT-5.3-Codex vs Muse Spark 1.3: questions
- Is GPT-5.3-Codex better than Muse Spark 1.3?
- GPT-5.3-Codex (xhigh) and Muse Spark 1.3 (max) are neck and neck on quality (62.9 vs 61.9, 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.
- GPT-5.3-Codex vs Muse Spark 1.3: which is cheaper, and will that change?
- Muse Spark 1.3 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 1.3 better for coding?
- They are level in coding (61 vs 61 on the category index). On IOI, the top shared coding benchmark, it is 53.8% for GPT-5.3-Codex against 56.6% for Muse Spark 1.3.
- Which is faster, GPT-5.3-Codex or Muse Spark 1.3?
- Muse Spark 1.3 streams ~2× faster (194 vs 114 output tokens per second, Artificial Analysis 7-day median).
- Is GPT-5.3-Codex or Muse Spark 1.3 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).
More questions (4)
- Which is better for long documents?
- They are level on long-context benchmarks (67 vs 66). Muse Spark 1.3 accepts more context: 1,048,576 against 400,000 tokens for GPT-5.3-Codex.
- Which has lower latency?
- Muse Spark 1.3 answers first: 46.3 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.
- Do independent benchmarks agree on GPT-5.3-Codex vs Muse Spark 1.3?
- Across the 8 benchmarks both have, GPT-5.3-Codex leads 1, Muse Spark 1.3 leads 7.
- When was Muse Spark 1.3 released?
- Muse Spark 1.3 was released on 2 Sep 2026.