MiniMax-M2 vs GPT-5.6 Terra
Head-to-head specifications
| Metric | MiniMax-M2 | GPT-5.6 Terra | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 55.0 | -45.5% |
| Context window | 262K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.36 | $1.14 | -68.4% |
| Output speed (tokens/s) | 77 | 138 | -44.2% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 30.0).
- MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 3.2× cheaper.
- GPT-5.6 Terra offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: MiniMax-M2 or GPT-5.6 Terra?
MiniMax-M2 advantages
- Affordability (+68%)
GPT-5.6 Terra advantages
- General intelligence (+45%)
- Context window (+74%)
- Output speed (+44%)
Which should you choose?
- Choose the MiniMax-M2 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Terra if you need the strongest overall reasoning and accuracy.
Value for money
MiniMax-M2 offers more intelligence per dollar (1.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
MiniMax-M2 vs GPT-5.6 Terra: which should you choose?
MiniMax-M2 — MiniMax multimodal model with an Intelligence Index of 30, a 262K-token context window and a blended price of $0.36/1M tokens (open weights).
GPT-5.6 Terra — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.14/1M tokens.
MiniMax-M2 vs GPT-5.6 Terra: GPT-5.6 Terra scores higher on the Intelligence Index. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 30.0). MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 3.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Terra scores 55.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Terra accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.6 Terra generates faster (138 vs 77 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiniMax-M2 is the cheaper model to run ($0.36 vs $1.14 per 1M tokens). MiniMax-M2 is open weights and GPT-5.6 Terra is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the MiniMax-M2 better than the GPT-5.6 Terra?
GPT-5.6 Terra takes the overall edge, though MiniMax-M2 wins in specific areas worth weighing. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 30.0).
What is the main difference between the MiniMax-M2 and the GPT-5.6 Terra?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 30.0). MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 3.2× cheaper.
Which is better value?
MiniMax-M2 offers more intelligence per dollar (1.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the MiniMax-M2 if you want the lowest cost per token at scale. Choose the GPT-5.6 Terra if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.