GPT-5.3-Codex vs Muse Ember
Muse Ember leads GPT-5.3-Codex on coding, 64.9 to 60.0 on the Vibe Coding Index, and Muse Ember costs 28.0x less per output token ($0.50 vs $14.00 per million). Updated November 14, 2026; 4 benchmarks have a result for both.
GPT-5.3-Codex or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 60.0 for GPT-5.3-Codex.
GPT-5.3-Codex is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 4
- GPT-5.3-Codex only
- 29
- Muse Ember only
- 11
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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.
Coding
Vibe Coding Index, six public coding benchmarks
Muse Ember
Muse Ember scores 64.9 against 60.0.
3 benchmarks behind each score
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1155.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.3-Codex $0.0088, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 28.8 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.3-Codex 1394 against Muse Ember 1616, 34.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Muse Ember $0.50 per million output tokens, 28.0x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Muse Ember 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.
| Category | GPT-5.3-Codex | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1406 | 1609 | Muse Ember |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 39.4% | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | 61.8% | 83.7% | Muse Ember |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | Not measured | 40.9% | Only Muse Ember measured |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | 1394 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1421 | 1621 | Muse Ember |
| LMArena WebDev: from an image | 1444 | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.0 | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1009 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 957 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1045 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| Tax Agent Bench (Vals AI) | Not measured | 38.2% | Only Muse Ember measured |
| Legal Research Bench (Vals AI) | Not measured | 49.0% | Only Muse Ember measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | Not measured | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-Codex measured |
| HLE-Diamond (Artificial Analysis run) | Not measured | 25.1% | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| Neither model has a result on the other 22 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 14 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| Neither model has a result on the other 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1155 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: UI components | 1127 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: game dev | 1151 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: data viz | 1161 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: 3D | 987 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1134 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: SVG | 1118 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1146 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Muse Ember | Leader |
|---|---|---|---|
| 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
Muse Ember costs less
Repository review
50K input + 3K output tokens
Muse Ember costs less
Agent loop
200K input + 10K output tokens
Muse Ember 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.
| Specification | OpenAI GPT-5.3-Codex | Meta Muse Ember |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | November 9, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.10 |
| Output $/Mtok | $14.00 | $0.50 |
| Coding index | 60.0 | 64.9 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 60.0 for GPT-5.3-Codex. GPT-5.3-Codex is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GPT-5.3-Codex or Muse Ember?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Muse Ember costs $0.10 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 Muse Ember?
Muse Ember, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs Muse Ember comparison based on?
4 benchmarks have a result for both models as of November 14, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.