Compare Models
Select models (max 7)
GPT 5.3 CodexMiniMax-M3.1-Flash
 |  |  |
|---|---|---|
| Vals IndexGDP-weighted benchmark | N/A | 53.78%±1.07 |
| Legal Research BenchAgentic US legal research | N/A | 34.13%±3.30 |
| Finance Agent v2.1Core financial analyst tasks | N/A | 58.83%±2.07 |
| Tax Agent BenchAgentic US corporate tax research | N/A | 32.14%±3.34 |
| Terminal-Bench 4.0Frontier-difficulty terminal tasks | N/A | 25.25%±1.01 |
| Vibe Code Bench v1.1Building web apps from scratch | 61.77%±4.71 | 73.94%±4.25 |
Benchmarks
Vals Index *
GPT 5.3 Codex
N/AMiniMax-M3.1-Flash
0.00%± 1.07
(26/26)Legal Research Bench *
GPT 5.3 Codex
N/AMiniMax-M3.1-Flash
0.00%± 3.30
(84/84)Finance Agent v2.1 *
GPT 5.3 Codex
N/AMiniMax-M3.1-Flash
0.00%± 2.07
(30/30)Tax Agent Bench *
GPT 5.3 Codex
N/AMiniMax-M3.1-Flash
0.00%± 3.34
(75/75)Terminal-Bench 4.0
GPT 5.3 Codex
N/AMiniMax-M3.1-Flash
0.00%± 1.01
(54/54)Vibe Code Bench v1.1 *
GPT 5.3 Codex
0.00%± 4.71
(118/118)MiniMax-M3.1-Flash
0.00%± 4.25
(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
N/A
vs.20.82%
Finance
N/A
vs.52.94%
Coding
70.23%
vs.49.60%
Cost Analysis
Per task and per token model pricing.
Vals Index · USD
N/A
$0.66
Cost / TestVals Index
N/A
vs.$0.66
Input Cost/ 1M Tokens
$1.75
vs.$0.15
Input Cache Read/ 1M Tokens
$0.175
vs.$0.015
Output Cost/ 1M Tokens
$14.00
vs.$0.60
Model Metadata
Basic information about each model.
| | |
|---|---|---|
| Model provider | OpenAI | MiniMax |
| Latency | N/A | 2597.84s |
| Cost (In/Out) | $1.75 / $14 | $0.15 / $0.6 |
| Context Window | 400k | 1M |
| Max Output Token | 128k | 524k |
| Input Modality |