Ember-1 vs GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k costs 3.8x less per output token than Ember-1 ($4.00 vs $15.00 per million). Updated October 22, 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
- $4.00
Ember-1 or GPT-3.5 Turbo 16k: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT-3.5 Turbo 16k has none, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo 16k for output tokens 3.8x cheaper ($4.00 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-3.5 Turbo 16k 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 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo 16k
Ember-1 $0.011, GPT-3.5 Turbo 16k $0.0050 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GPT-3.5 Turbo 16k 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-3.5 Turbo 16k $4.00 per million output tokens, 3.8x apart.
- Context window
- Ember-1 takes 1.05M tokens, GPT-3.5 Turbo 16k 16K.
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-3.5 Turbo 16k | 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 38 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GPT-3.5 Turbo 16k | 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 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 22 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 47 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-3.5 Turbo 16k costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo 16k costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo 16k 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-3.5 Turbo 16k |
|---|---|---|
| Maker | Fireworks | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 24, 2026 | August 28, 2023 |
| Context window | 1.05M | 16K |
| Input $/Mtok | $3.00 | $3.00 |
| Output $/Mtok | $15.00 | $4.00 |
| 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-3.5 Turbo 16k for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GPT-3.5 Turbo 16k has none, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo 16k for output tokens 3.8x cheaper ($4.00 vs $15.00 per million).
Which is cheaper, Ember-1 or GPT-3.5 Turbo 16k?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GPT-3.5 Turbo 16k costs $3.00 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GPT-3.5 Turbo 16k?
Ember-1, with 1.05M tokens against 16K.
Has any benchmark tested both Ember-1 and GPT-3.5 Turbo 16k?
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-3.5 Turbo 16k has none yet. Compare them on price and context instead. Data as of October 22, 2026.