Ember-1 vs GPT Audio Mini
GPT Audio Mini costs 6.3x less per output token than Ember-1 ($2.40 vs $15.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.40
Ember-1 or GPT Audio Mini: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT Audio Mini has none, so the two cannot be ranked on coding.
Pick GPT Audio Mini for output tokens 6.3x cheaper ($2.40 vs $15.00 per million).
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 Audio Mini has none yet. 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
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
GPT Audio Mini
Ember-1 $0.011, GPT Audio Mini $0.0018 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GPT Audio Mini 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
- Ember-1 $15.00 against GPT Audio Mini $2.40 per million output tokens, 6.3x apart.
- Context window
- Ember-1 takes 1.05M tokens, GPT Audio Mini 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 Index5 boards measured
| Benchmark | Ember-1 | GPT Audio Mini | Leader |
|---|---|---|---|
| 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 |
| Neither model has a result on the other 33 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GPT Audio Mini | 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 |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GPT Audio Mini | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 20 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GPT Audio Mini | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 45 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
GPT Audio Mini costs less
Repository review
50K input + 3K output tokens
GPT Audio Mini costs less
Agent loop
200K input + 10K output tokens
GPT Audio Mini 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 Audio Mini |
|---|---|---|
| Maker | Fireworks | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 24, 2026 | January 19, 2026 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $3.00 | $0.60 |
| Output $/Mtok | $15.00 | $2.40 |
| 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 Audio Mini for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT Audio Mini has none, so the two cannot be ranked on coding. Pick GPT Audio Mini for output tokens 6.3x cheaper ($2.40 vs $15.00 per million).
Which is cheaper, Ember-1 or GPT Audio Mini?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GPT Audio Mini costs $0.60 and $2.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GPT Audio Mini?
Ember-1, with 1.05M tokens against 128K.
Has any benchmark tested both Ember-1 and GPT Audio Mini?
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 Audio Mini has none yet. Compare them on price and context instead. Data as of October 21, 2026.