Compare Models
Select models (max 7)
Ember-1GPT-5.6 Luna
 |  |  |
|---|---|---|
| Legal Research BenchAgentic US legal research | 44.71%±3.46 | 36.54%±3.35 |
| Tax Agent BenchAgentic US corporate tax research | 31.08%±3.34 | 24.82%±3.11 |
| MedCodeMedical billing code support | N/A | 42.39%±2.27 |
| Terminal-Bench-ScienceExpert-authored scientific research workflows | 5.71%±2.79 | 0.00%±0.00 |
| Terminal-Bench 4.0Frontier-difficulty terminal tasks | 20.71%±3.64 | 11.62%±1.01 |
| Vibe Code Bench v1.1Building web apps from scratch | 83.87%±3.15 | 77.06%±3.07 |
Benchmarks
Legal Research Bench *
Ember-1
0.00%± 3.46
(84/84)GPT-5.6 Luna
0.00%± 3.35
(84/84)Tax Agent Bench *
Ember-1
0.00%± 3.34
(75/75)GPT-5.6 Luna
0.00%± 3.11
(75/75)MedCode *
Ember-1
N/AGPT-5.6 Luna
0.00%± 2.27
(107/107)Terminal-Bench-Science
Ember-1
0.00%± 2.79
(42/42)GPT-5.6 Luna
0.00%± 0.00
(42/42)Terminal-Bench 4.0
Ember-1
0.00%± 3.64
(54/54)GPT-5.6 Luna
0.00%± 1.01
(54/54)Vibe Code Bench v1.1 *
Ember-1
0.00%± 3.15
(118/118)GPT-5.6 Luna
0.00%± 3.07
(118/118)Overall performance
Performance on the Vals Index, a GDP-weighted aggregation of tasks across finance, coding, and law
•2026-10-22
Comparison by Industry
Model performance on different sections of the economy.
Legal
44.71%
vs.60.29%
Finance
31.08%
vs.56.09%
Healthcare
N/A
vs.63.39%
Math
61.00%
vs.60.00%
Science
31.76%
vs.25.30%
Academic
N/A
vs.87.58%
Education
N/A
vs.44.22%
Coding
50.30%
vs.46.64%
Cyber
69.52%
vs.37.58%
Social Mobility
66.17%
vs.61.16%
Cost Analysis
Per task and per token model pricing.
Vals Index · USD
N/A
N/A
Cost / TestVals Index
N/A
vs.N/A
Input Cost/ 1M Tokens
$3.00
vs.$0.20
Input Cache Write/ 1M Tokens
N/A
vs.$0.25
Input Cache Read/ 1M Tokens
$0.30
vs.$0.02
Output Cost/ 1M Tokens
$15.00
vs.$1.20
Model Metadata
Basic information about each model.
| | |
|---|---|---|
| Model provider | Fireworks | OpenAI |
| Latency | N/A | N/A |
| Cost (In/Out) | $3 / $15 | $0.2 / $1.2 |
| Context Window | 1M | 1M |
| Max Output Token | 131k | 128k |
| Input Modality |