Ember-1 vs GPT-4o (2024-05-13)
Ember-1 and GPT-4o (2024-05-13) cost about the same per output token ($15.00 vs $15.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- 27.1
- Output $/M
- $15.00
Ember-1 or GPT-4o (2024-05-13): which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); GPT-4o (2024-05-13) on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead.
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
No call
Only Ember-1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-4o (2024-05-13) has a AA Intelligence Index result, 7.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ember-1
Ember-1 $0.011, GPT-4o (2024-05-13) $0.013 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GPT-4o (2024-05-13) not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Both cost $15.00 per million output tokens.
- Context window
- Ember-1 takes 1.05M tokens, GPT-4o (2024-05-13) 128K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 24.2 | Only GPT-4o (2024-05-13) measured |
| Terminal-Bench 4 (Vals AI) | 20.7% | Not measured | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | 83.9% | Not measured | Only Ember-1 measured |
| Code migration (Vals AI) | 32.5% | Not measured | Only Ember-1 measured |
| IOI (Vals AI) | 46.3% | Not measured | Only Ember-1 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Ember-1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 33.4% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: coding | Not measured | 1297 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: software and IT | Not measured | 1297 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.3 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 51.8% | Not measured | Only Ember-1 measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only Ember-1 measured |
| Legal Research Bench (Vals AI) | 44.7% | Not measured | Only Ember-1 measured |
| Excel Modeling Benchmark (Vals AI) | 63.4% | Not measured | Only Ember-1 measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Ember-1 measured |
| Vals Science Index | 31.8% | Not measured | Only Ember-1 measured |
| METR time horizons | Not measured | 40.8% | Only GPT-4o (2024-05-13) measured |
| BALROG | Not measured | 32.3% | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 50.2% | Only GPT-4o (2024-05-13) measured |
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 52.6% | Only GPT-4o (2024-05-13) measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 1.8% | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.9% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | Not measured | 1281 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: expert | Not measured | 1250 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts in English | Not measured | 1293 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 6.3% | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | Not measured | 51.0% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | Not measured | 1284 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | Not measured | 1284 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 74.0% | Only GPT-4o (2024-05-13) measured |
| MMLU | Not measured | 84.2% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | Not measured | 1295 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: medicine and healthcare | Not measured | 1278 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: legal and government | Not measured | 1314 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | Not measured | 1261 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards measured
| Benchmark | Ember-1 | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| LMArena Text | Not measured | 1300 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: instruction following | Not measured | 1278 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: creative writing | Not measured | 1291 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision | Not measured | 1137 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | Not measured | 1312 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: non-English | Not measured | 1283 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Chinese | Not measured | 1277 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: German | Not measured | 1282 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | Not measured | 1257 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | Not measured | 1234 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | Not measured | 1304 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | Not measured | 1292 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | Not measured | 1285 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Polish | Not measured | 1365 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | Not measured | 1302 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: longer queries | Not measured | 1288 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: writing and language | Not measured | 1298 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: entertainment and media | Not measured | 1289 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: English | Not measured | 1147 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | Not measured | 1132 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 27 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
Ember-1 costs less
Repository review
50K input + 3K output tokens
Ember-1 costs less
Agent loop
200K input + 10K output tokens
Ember-1 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 | Fireworks Ember-1 | OpenAI GPT-4o (2024-05-13) |
|---|---|---|
| Maker | Fireworks | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 24, 2026 | May 13, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $3.00 | $5.00 |
| Output $/Mtok | $15.00 | $15.00 |
| Coding index | 52.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than GPT-4o (2024-05-13) for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Ember-1 or GPT-4o (2024-05-13)?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GPT-4o (2024-05-13) costs $5.00 and $15.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GPT-4o (2024-05-13)?
Ember-1, with 1.05M tokens against 128K.
Has any benchmark tested both Ember-1 and GPT-4o (2024-05-13)?
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); GPT-4o (2024-05-13) on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead. Data as of October 23, 2026.