DeepSeek V4 Flash 0423 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
- 24.4
- Output $/M
- $0.28
- 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 V4 Flash 0423 has none, so the two cannot be ranked on coding.
DeepSeek V4 Flash 0423 has the edge where output tokens cost 53.6x less.
- Shared results
- 0
- DeepSeek V4 Flash 0423 only
- 48
- 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
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4 Flash 0423 has a AA Intelligence Index result, 24.4.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0423 has a AA Agentic Index result, 26.3.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0423 has a Design Arena: websites result, 1208.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 $0.0003, Ember-1 $0.011 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4 Flash 0423 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 V4 Flash 0423 $0.28 against Ember-1 $15.00 per million output tokens, 53.6x apart.
- Context window
- Both take 1.05M tokens.
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 Index13 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| AA Coding Index | 52.0 | Not measured | Only DeepSeek V4 Flash 0423 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 |
| WeirdML | 45.6% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| ALE-Bench | 678.2 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: coding | 1457 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: React | 1585 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: HTML | 1558 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Epoch Capabilities Index | 146.1 | Not measured | Only DeepSeek V4 Flash 0423 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| 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 31 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 22.5% | Only Ember-1 measured |
| SimpleBench | 46.3% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts | 1444 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: expert | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts in English | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| LMArena Text: math | 1426 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: mathematical work | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| LMArena Text: science | 1446 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: medicine and healthcare | 1448 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: legal and government | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: business and finance | 1425 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1208 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: UI components | 1165 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: game dev | 1197 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: data viz | 1127 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: 3D | 1189 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: all code categories | 1205 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: SVG | 1155 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: ASCII art | 1109 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Ember-1 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 66.2% | Only Ember-1 measured |
| DTBench | 86.4% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMCA | 35.9% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: instruction following | 1421 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: creative writing | 1403 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: non-English | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Chinese | 1463 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: German | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Japanese | 1389 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Korean | 1383 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: French | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Russian | 1427 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Polish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: multi-turn | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: longer queries | 1434 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: writing and language | 1410 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | 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 V4 Flash 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0423 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 V4 Flash 0423 | Fireworks Ember-1 |
|---|---|---|
| Maker | DeepSeek | Fireworks |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | September 24, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.14 | $3.00 |
| Output $/Mtok | $0.28 | $15.00 |
| Coding index | Not benchmarked | 52.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than Ember-1 for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. DeepSeek V4 Flash 0423 has the edge where output tokens cost 53.6x less.
Which is cheaper, DeepSeek V4 Flash 0423 or Ember-1?
DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 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 V4 Flash 0423 or Ember-1?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Flash 0423 vs Ember-1 comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Ember-1's on 2. Where only one model has a result, the page says so instead of naming a winner.