Rank #12
MiniMax: MiniMax M3
- Multimodal
- 1.0M ctx
- 1M
- $0.75
Model · Vendors
Ranked models from this vendor or team on Global rankings (same data snapshot as the main global board).
Board snapshot:
MiniMax ships abab and speech/multimodal models popular in Chinese consumer and enterprise apps, mirrored on OpenRouter. Compare MiniMax ranks, long-context metrics, and blended token prices on this vendor hub.
Rank #12
Rank #92
| Rank | Name | Type | Key metric | 1M tokens (avg) |
|---|---|---|---|---|
| 12 | MiniMax: MiniMax M3 | Multimodal | 1.0M ctx | $0.75 |
| 92 | MiniMax: MiniMax-01 | Multimodal | 1.0M ctx | $0.65 |
| Rank | Name | Size | Price | Notes |
|---|---|---|---|---|
| — | MiniMax: MiniMax M1 | 1.0M ctx | $1.38/1M | minimax/minimax-m1 |
| — | MiniMax: MiniMax M2 | 205k ctx | $0.64/1M | minimax/minimax-m2 |
| — | MiniMax: MiniMax M2-her | 66k ctx | $0.75/1M | minimax/minimax-m2-her |
| — | MiniMax: MiniMax M2.1 | 205k ctx | $0.75/1M | minimax/minimax-m2.1 |
| — | MiniMax: MiniMax M2.5 | 205k ctx | $0.53/1M | minimax/minimax-m2.5 |
| — | MiniMax: MiniMax M2.7 | 205k ctx | $0.63/1M | minimax/minimax-m2.7 |
| — | MiniMax: MiniMax M3 | 1.0M ctx | $0.75/1M | minimax/minimax-m3 |
| — | MiniMax: MiniMax-01 | 1.0M ctx | $0.65/1M | minimax/minimax-01 |
Counts and prices come from the OpenRouter catalog snapshot merged with AI Hippo Global rankings (same snapshot as /rankings/). Rank reflects the composite score on that board—not LMSYS Chatbot Arena ELO. Snapshot date: 2026-08-02. Methodology
MiniMax: MiniMax M3 currently ranks #12 on AI Hippo's Global rankings board (snapshot 2026-08-02).
Across catalog and ranked rows, blended 1M-token prices range from $0.53 to $1.38 in the latest snapshot (2026-08-02). Check individual model pages for prompt vs completion splits.
2 of 8 catalog models appear on Global rankings; the remaining 6 are listed under catalog-only on this page.
Yes—use the compare link on this page or open /en/compare/?m=MiniMax%3A%20MiniMax%20M3&m=MiniMax%3A%20MiniMax-01 to side-by-side context, price, and composite score for the top ranked models.
Global rankings uses on-site weighting (context window first, then blended 1M-token price)—not third-party ELO. Full methodology: /en/methodology/