DeepSeek V3 vs Ember-1
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.9594
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
Decision reading
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; DeepSeek V3 has none, so the two cannot be ranked on coding.
DeepSeek V3 has the edge where output tokens cost 15.6x less.
- Shared results
- 0
- DeepSeek V3 only
- 13
- Ember-1 only
- 14
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3
DeepSeek V3 $0.0008, Ember-1 $0.011 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 not benchmarked, Ember-1 52.6 from 2 benchmarks.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- DeepSeek V3 $0.9594 against Ember-1 $15.00 per million output tokens, 15.6x apart.
- Context window
- DeepSeek V3 takes 164K tokens, Ember-1 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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 20.7% | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | Not measured | 83.9% | Only Ember-1 measured |
| Code migration (Vals AI) | Not measured | 32.5% | Only Ember-1 measured |
| IOI (Vals AI) | Not measured | 46.3% | Only Ember-1 measured |
| CyberBench (Vals AI) | Not measured | 69.5% | Only Ember-1 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 51.8% | Only Ember-1 measured |
| Tax Agent Bench (Vals AI) | Not measured | 31.1% | Only Ember-1 measured |
| Legal Research Bench (Vals AI) | Not measured | 44.7% | Only Ember-1 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 63.4% | Only Ember-1 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 5.7% | Only Ember-1 measured |
| Vals Science Index | Not measured | 31.8% | 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 index2 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 22.5% | Only Ember-1 measured |
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 48.9% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | Not measured | Only DeepSeek V3 measured |
| Design Arena: UI components | 1093 | Not measured | Only DeepSeek V3 measured |
| Design Arena: game dev | 1053 | Not measured | Only DeepSeek V3 measured |
| Design Arena: data viz | 1089 | Not measured | Only DeepSeek V3 measured |
| Design Arena: 3D | 1087 | Not measured | Only DeepSeek V3 measured |
| Design Arena: all code categories | 1108 | Not measured | Only DeepSeek V3 measured |
| Design Arena: SVG | 977 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 66.2% | Only Ember-1 measured |
| DTBench | 62.7% | Not measured | Only DeepSeek V3 measured |
| LMCA | 15.2% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Ember-1 | 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
DeepSeek V3 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 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 | DeepSeek V3 | Fireworks Ember-1 |
|---|---|---|
| Maker | DeepSeek | Fireworks |
| Weights | Open weights | No public weights listed |
| Released | December 26, 2024 | September 24, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.2887 | $3.00 |
| Output $/Mtok | $0.9594 | $15.00 |
| Coding index | Not benchmarked | 52.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 better than Ember-1 for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; DeepSeek V3 has none, so the two cannot be ranked on coding. DeepSeek V3 has the edge where output tokens cost 15.6x less.
Which is cheaper, DeepSeek V3 or Ember-1?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 per million output tokens; Ember-1 costs $3.00 and $15.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 or Ember-1?
Ember-1, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3 vs Ember-1 comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3's coding index rests on 0 independent benchmarks and Ember-1's on 2. Where only one model has a result, the page says so instead of naming a winner.