Ember-1 vs Qwen3 30B A3B
Qwen3 30B A3B costs 30.0x less per output token than Ember-1 ($0.50 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
- 38.2
- Output $/M
- $0.50
Ember-1 or Qwen3 30B A3B: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding.
Pick Qwen3 30B A3B for output tokens 30.0x cheaper ($0.50 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); Qwen3 30B A3B on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML. 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 Qwen3 30B A3B has a AA Intelligence Index result, 7.6.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 30B A3B has a Design Arena: websites result, 954.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B
Ember-1 $0.011, Qwen3 30B A3B $0.0004 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, Qwen3 30B A3B 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 Qwen3 30B A3B $0.50 per million output tokens, 30.0x apart.
- Context window
- Ember-1 takes 1.05M tokens, Qwen3 30B A3B 41K.
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 Index10 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 50.6% | Only Qwen3 30B A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.8% | Only Qwen3 30B A3B measured |
| WeirdML | Not measured | 29.8% | Only Qwen3 30B A3B measured |
| LMArena Text: coding | Not measured | 1337 | Only Qwen3 30B A3B measured |
| LMArena Text: software and IT | Not measured | 1340 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.6 | Only Qwen3 30B A3B measured |
| Epoch Capabilities Index | Not measured | 137.3 | Only Qwen3 30B A3B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | 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 |
| τ²-Bench Telecom | Not measured | 26.0% | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 61.6% | Only Qwen3 30B A3B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.2% | Only Qwen3 30B A3B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 61.7% | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: expert | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts in English | Not measured | 1329 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 72.3 | Only Qwen3 30B A3B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 72.3% | Only Qwen3 30B A3B measured |
| OTIS Mock AIME | Not measured | 62.8% | Only Qwen3 30B A3B measured |
| LMArena Text: math | Not measured | 1355 | Only Qwen3 30B A3B measured |
| LMArena Text: mathematical work | Not measured | 1363 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.7% | Only Qwen3 30B A3B measured |
| LMArena Text: science | Not measured | 1321 | Only Qwen3 30B A3B measured |
| LMArena Text: medicine and healthcare | Not measured | 1318 | Only Qwen3 30B A3B measured |
| LMArena Text: legal and government | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: business and finance | Not measured | 1309 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 954 | Only Qwen3 30B A3B measured |
| Design Arena: UI components | Not measured | 943 | Only Qwen3 30B A3B measured |
| Design Arena: game dev | Not measured | 898 | Only Qwen3 30B A3B measured |
| Design Arena: data viz | Not measured | 967 | Only Qwen3 30B A3B measured |
| Design Arena: all code categories | Not measured | 941 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards measured
| Benchmark | Ember-1 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 41.5% | Only Qwen3 30B A3B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 30B A3B measured |
| DTBench | Not measured | 60.3% | Only Qwen3 30B A3B measured |
| LMCA | Not measured | 15.8% | Only Qwen3 30B A3B measured |
| Fiction.LiveBench | Not measured | 40.6% | Only Qwen3 30B A3B measured |
| Creative writing (Lech Mazur) | Not measured | 75.3% | Only Qwen3 30B A3B measured |
| LMArena Text | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: instruction following | Not measured | 1283 | Only Qwen3 30B A3B measured |
| LMArena Text: creative writing | Not measured | 1271 | Only Qwen3 30B A3B measured |
| LMArena Text: English | Not measured | 1335 | Only Qwen3 30B A3B measured |
| LMArena Text: non-English | Not measured | 1295 | Only Qwen3 30B A3B measured |
| LMArena Text: Chinese | Not measured | 1347 | Only Qwen3 30B A3B measured |
| LMArena Text: German | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: Japanese | Not measured | 1254 | Only Qwen3 30B A3B measured |
| LMArena Text: Korean | Not measured | 1261 | Only Qwen3 30B A3B measured |
| LMArena Text: French | Not measured | 1352 | Only Qwen3 30B A3B measured |
| LMArena Text: Spanish | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: Russian | Not measured | 1291 | Only Qwen3 30B A3B measured |
| LMArena Text: Polish | Not measured | 1299 | Only Qwen3 30B A3B measured |
| LMArena Text: multi-turn | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: longer queries | Not measured | 1312 | Only Qwen3 30B A3B measured |
| LMArena Text: writing and language | Not measured | 1290 | Only Qwen3 30B A3B measured |
| LMArena Text: entertainment and media | Not measured | 1258 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 22 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
Qwen3 30B A3B costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B 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 | Qwen3 30B A3B |
|---|---|---|
| Maker | Fireworks | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | April 28, 2025 |
| Context window | 1.05M | 41K |
| Input $/Mtok | $3.00 | $0.12 |
| Output $/Mtok | $15.00 | $0.50 |
| 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 Qwen3 30B A3B for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B for output tokens 30.0x cheaper ($0.50 vs $15.00 per million).
Which is cheaper, Ember-1 or Qwen3 30B A3B?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or Qwen3 30B A3B?
Ember-1, with 1.05M tokens against 41K.
Has any benchmark tested both Ember-1 and Qwen3 30B A3B?
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); Qwen3 30B A3B on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML. Compare them on price and context instead. Data as of October 22, 2026.