Ember-1 vs Gemma 3 27B
Gemma 3 27B costs 50.0x less per output token than Ember-1 ($0.30 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
- 31.4
- Output $/M
- $0.30
Ember-1 or Gemma 3 27B: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Gemma 3 27B has none, so the two cannot be ranked on coding.
Pick Gemma 3 27B for output tokens 50.0x cheaper ($0.30 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); Gemma 3 27B on Aider polyglot, AA Coding Index and LiveCodeBench (Artificial Analysis run). 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 Gemma 3 27B has a AA Intelligence Index result, 4.9.
Directional only
Agentic work
AA Agentic Index
No call
Only Gemma 3 27B has a AA Agentic Index result, 0.1.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 27B
Ember-1 $0.011, Gemma 3 27B $0.0002 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, Gemma 3 27B 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 Gemma 3 27B $0.30 per million output tokens, 50.0x apart.
- Context window
- Ember-1 takes 1.05M tokens, Gemma 3 27B 131K.
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 Index13 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 4.9% | Only Gemma 3 27B measured |
| AA Coding Index | Not measured | 10.1 | Only Gemma 3 27B measured |
| 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 | 13.7% | Only Gemma 3 27B measured |
| SciCode | Not measured | 23.3% | Only Gemma 3 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Gemma 3 27B measured |
| LMArena Text: coding | Not measured | 1322 | Only Gemma 3 27B measured |
| LMArena Text: software and IT | Not measured | 1354 | Only Gemma 3 27B measured |
| SciCode | Not measured | 0.0% | Only Gemma 3 27B measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 4.9 | Only Gemma 3 27B measured |
| Epoch Capabilities Index | Not measured | 130.0 | Only Gemma 3 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.1 | Only Gemma 3 27B measured |
| 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 | 10.5% | Only Gemma 3 27B measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 29.5% | Only Gemma 3 27B measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 42.8% | Only Gemma 3 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.4% | Only Gemma 3 27B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 47.7% | Only Gemma 3 27B measured |
| LMArena Text: hard prompts | Not measured | 1340 | Only Gemma 3 27B measured |
| LMArena Text: expert | Not measured | 1304 | Only Gemma 3 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1353 | Only Gemma 3 27B measured |
| LMArena Vision: diagrams | Not measured | 1156 | Only Gemma 3 27B measured |
| LMArena Vision: homework | Not measured | 1160 | Only Gemma 3 27B measured |
| Kagi LLM Benchmark | Not measured | 35.1% | Only Gemma 3 27B measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 20.7 | Only Gemma 3 27B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 20.7% | Only Gemma 3 27B measured |
| OTIS Mock AIME | Not measured | 22.5% | Only Gemma 3 27B measured |
| MATH level 5 | Not measured | 74.0% | Only Gemma 3 27B measured |
| LMArena Text: math | Not measured | 1312 | Only Gemma 3 27B measured |
| LMArena Text: mathematical work | Not measured | 1327 | Only Gemma 3 27B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 66.9% | Only Gemma 3 27B measured |
| GeoBench | Not measured | 52.0% | Only Gemma 3 27B measured |
| LMArena Text: science | Not measured | 1383 | Only Gemma 3 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1391 | Only Gemma 3 27B measured |
| LMArena Text: legal and government | Not measured | 1375 | Only Gemma 3 27B measured |
| LMArena Text: business and finance | Not measured | 1360 | Only Gemma 3 27B measured |
| FACTS Benchmark Suite | Not measured | 29.1% | Only Gemma 3 27B measured |
| Neither model has a result on the other 11 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index32 boards measured
| Benchmark | Ember-1 | Gemma 3 27B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 31.8% | Only Gemma 3 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 7.3% | Only Gemma 3 27B measured |
| DTBench | Not measured | 52.5% | Only Gemma 3 27B measured |
| LMCA | Not measured | 12.3% | Only Gemma 3 27B measured |
| Fiction.LiveBench | Not measured | 33.3% | Only Gemma 3 27B measured |
| Creative writing (Lech Mazur) | Not measured | 79.9% | Only Gemma 3 27B measured |
| LMArena Text | Not measured | 1358 | Only Gemma 3 27B measured |
| LMArena Text: instruction following | Not measured | 1321 | Only Gemma 3 27B measured |
| LMArena Text: creative writing | Not measured | 1345 | Only Gemma 3 27B measured |
| LMArena Vision | Not measured | 1165 | Only Gemma 3 27B measured |
| LMArena Text: English | Not measured | 1377 | Only Gemma 3 27B measured |
| LMArena Text: non-English | Not measured | 1334 | Only Gemma 3 27B measured |
| LMArena Text: Chinese | Not measured | 1346 | Only Gemma 3 27B measured |
| LMArena Text: German | Not measured | 1362 | Only Gemma 3 27B measured |
| LMArena Text: Japanese | Not measured | 1287 | Only Gemma 3 27B measured |
| LMArena Text: Korean | Not measured | 1308 | Only Gemma 3 27B measured |
| LMArena Text: French | Not measured | 1368 | Only Gemma 3 27B measured |
| LMArena Text: Spanish | Not measured | 1349 | Only Gemma 3 27B measured |
| LMArena Text: Russian | Not measured | 1349 | Only Gemma 3 27B measured |
| LMArena Text: Polish | Not measured | 1345 | Only Gemma 3 27B measured |
| LMArena Text: multi-turn | Not measured | 1345 | Only Gemma 3 27B measured |
| LMArena Text: longer queries | Not measured | 1333 | Only Gemma 3 27B measured |
| LMArena Text: writing and language | Not measured | 1347 | Only Gemma 3 27B measured |
| LMArena Text: entertainment and media | Not measured | 1320 | Only Gemma 3 27B measured |
| LMArena Vision: English | Not measured | 1188 | Only Gemma 3 27B measured |
| LMArena Vision: Chinese | Not measured | 1164 | Only Gemma 3 27B measured |
| LMArena Vision: reading text | Not measured | 1156 | Only Gemma 3 27B measured |
| LMArena Vision: humor | Not measured | 1166 | Only Gemma 3 27B measured |
| LMArena Vision: captioning | Not measured | 1151 | Only Gemma 3 27B measured |
| LMArena Vision: recognizing things | Not measured | 1196 | Only Gemma 3 27B measured |
| LMArena Vision: creative writing | Not measured | 1177 | Only Gemma 3 27B measured |
| Neither model has a result on the other 14 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
Gemma 3 27B costs less
Repository review
50K input + 3K output tokens
Gemma 3 27B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 27B 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 | Google Gemma 3 27B |
|---|---|---|
| Maker | Fireworks | |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | March 12, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $3.00 | $0.08 |
| Output $/Mtok | $15.00 | $0.30 |
| 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 Gemma 3 27B for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Gemma 3 27B has none, so the two cannot be ranked on coding. Pick Gemma 3 27B for output tokens 50.0x cheaper ($0.30 vs $15.00 per million).
Which is cheaper, Ember-1 or Gemma 3 27B?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; Gemma 3 27B costs $0.08 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or Gemma 3 27B?
Ember-1, with 1.05M tokens against 131K.
Has any benchmark tested both Ember-1 and Gemma 3 27B?
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); Gemma 3 27B on Aider polyglot, AA Coding Index and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.