MiniMax-M2.7-highspeed vs Nemotron Cascade 2 30B A3B vs Trinity Large Thinking
Trinity Large Thinking comes out ahead, 40 to 35 and 34 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7-highspeed
34/100- ECI—
- Price$0.60 / $2.40
- Context205K
NVIDIANemotron Cascade 2 30B A3B
35/100- ECI—
- Price—
- Context256K
- Our pick
Arcee AITrinity Large Thinking
40/100- ECI—
- Price$0.25 / $0.80
- Context524K
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 40/100 against Nemotron Cascade 2 30B A3B (35) and MiniMax-M2.7-highspeed (34). It leads on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceTrinity Large ThinkingTrinity Large Thinking $0.388 · MiniMax-M2.7-highspeed $1.05 per 1M tokens (3:1 blend) · Nemotron Cascade 2 30B A3B unpriced
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Nemotron Cascade 2 30B A3B 256,000 · MiniMax-M2.7-highspeed 204,800 tokens
- Widest inputsSame inputsMiniMax-M2.7-highspeed: Text · Nemotron Cascade 2 30B A3B: Text · Trinity Large Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | MiniMax-M2.7-highspeed | Nemotron Cascade 2 30B A3B | Trinity Large Thinking |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 35 | 35 |
| Context window | 40% | 32 | 36 | 49 |
| Overall | 100% | 34/100 | 35/100 | 40/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7-highspeedMiniMax | Nemotron Cascade 2 30B A3BNVIDIA | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | — | $0.25 (best) |
| Output | $2.40 | — | $0.80 (best) |
| Cached input | $0.06 | — | $0.06 |
| Blended (3:1) | $1.05 | — | $0.388 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official MiniMax (minimax.io) API | — | Official Arcee API |
| Limits | |||
| Context window | 204,800 tokens | 256,000 tokens | 524,288 tokens (best) |
| Max output | 131,072 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenOpenMDW-1.1 |
| API model ID | MiniMax-M2.7-highspeed | — | trinity-large-thinking |
| API providers | 14 (best) | — | 6 |
| Released | Mar 18, 2026 | Mar 24, 2026 | Apr 1, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M2.7-highspeed$10.80
- Nemotron Cascade 2 30B A3B—
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: MiniMax-M2.7-highspeed, Nemotron Cascade 2 30B A3B or Trinity Large Thinking?
Trinity Large Thinking is the better all-round choice, scoring 40/100 against Nemotron Cascade 2 30B A3B (35) and MiniMax-M2.7-highspeed (34). It leads on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MiniMax-M2.7-highspeed, Nemotron Cascade 2 30B A3B or Trinity Large Thinking?
Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). MiniMax-M2.7-highspeed costs $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $1.05 for MiniMax-M2.7-highspeed (2.7× as much). Nemotron Cascade 2 30B A3B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.7-highspeed has not been scored yet, Nemotron Cascade 2 30B A3B has not been scored yet and Trinity Large Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7-highspeed, Nemotron Cascade 2 30B A3B and Trinity Large Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 256,000 for Nemotron Cascade 2 30B A3B and 204,800 for MiniMax-M2.7-highspeed. Maximum output per response: MiniMax-M2.7-highspeed up to 131,072, Nemotron Cascade 2 30B A3B up to 32,768, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7-highspeed accepts text; Nemotron Cascade 2 30B A3B accepts text; Trinity Large Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (OpenMDW-1.1), so you can self-host them.
Which is newer?
Trinity Large Thinking is the newest, released Apr 1, 2026. Nemotron Cascade 2 30B A3B came out Mar 24, 2026; MiniMax-M2.7-highspeed came out Mar 18, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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