What other models does MiniMax offer?

MiniMax builds its M2 series on a shared 230-billion-parameter mixture-of-experts architecture, releasing progressive updates that trade higher reasoning quality against speed across model variants. Here are some other models MiniMax supports:

• MiniMax M2.5: MiniMax M2.5 is MiniMax's earlier reasoning model, released in February 2026 under an MIT license. It scores 42 on the Artificial Analysis Intelligence Index, ranking #17 of 88 comparable models, and is the fastest option in the M2 family at 191.0 tokens per second, making it the right choice when throughput takes priority over reasoning quality

• MiniMax Text-01: MiniMax Text-01 is MiniMax's earlier general-purpose model, available before the M2 reasoning series and suited for applications that do not require chain-of-thought processing and prefer a more established, widely tested checkpoint

How does MiniMax M2.7 differ from MiniMax's other models?

MiniMax M2.7 is the highest-capability model currently available from MiniMax, sitting above M2.5 in reasoning quality while running at a lower output speed.

• Intelligence ranking: MiniMax M2.7 scores 50 on the Artificial Analysis Intelligence Index, ranking #7 of 88 comparable models. MiniMax M2.5 scores 42 and ranks #17, placing M2.7 meaningfully ahead on complex reasoning tasks

• Speed: MiniMax M2.7 generates 60.7 tokens per second, ranking #26 of 88 comparable models. MiniMax M2.5 is more than three times faster at 191.0 tokens per second. M2.7's speed is competitive but not a standout for applications where throughput is the primary constraint

• Pricing: MiniMax M2.7 and M2.5 share the same input and output prices: $0.30 per 1M input tokens and $1.20 per 1M output tokens. M2.7's lower blended rate of $0.22 per 1M tokens (versus $0.29 for M2.5) reflects its more cache-efficient generation pattern

• Context window: Both M2.7 and M2.5 support a 205k-token context window, providing comparable long-context capacity for retrieval-heavy or multi-turn workloads

• License: MiniMax M2.7 is released under a non-commercial license; commercial use requires a separate agreement with MiniMax. MiniMax M2.5 is MIT-licensed, making it the more accessible option for commercial teams that want open-weight usage without negotiating a license

MiniMax M2.7 is the better fit when reasoning accuracy on complex tasks is the deciding factor and your use case can tolerate a lower output speed compared to M2.5.

What models should I consider using alongside MiniMax M2.7?

No single model is optimal for every task. Here are models worth pairing with MiniMax M2.7 depending on what your product needs:

• MiniMax M2.5 (MiniMax): For the portion of your traffic that does not require deep reasoning and benefits from fast responses, route to MiniMax M2.5. At 191 tokens per second, it handles high-volume, lower-complexity requests at the same price point while reserving M2.7 for tasks that need its higher Intelligence Index score

• Claude Opus 4 (Anthropic): When your product handles complex multi-step reasoning over sensitive or proprietary content where provider accountability and SLA guarantees are important, Claude Opus 4 provides a well-documented reasoning alternative from a provider with broad enterprise compliance coverage

• Gemini 2.5 Pro (Google): For multimodal tasks that combine text and image inputs, Gemini 2.5 Pro covers modalities that MiniMax M2.7 does not support, and its reasoning capability makes it a strong routing target for vision-plus-reasoning pipelines

• DeepSeek R1 (DeepSeek): For open-weight reasoning workloads where self-hosting or multi-provider redundancy is a goal, DeepSeek R1 competes on reasoning benchmarks and offers additional deployment flexibility as a fallback to MiniMax M2.7

• GPT-4o mini (OpenAI): For simple extraction, classification, or short-form generation tasks that do not require M2.7's reasoning capability, GPT-4o mini provides a well-supported, low-cost option that reduces spend on straightforward requests in your pipeline

What are the challenges of using MiniMax M2.7 in my product?

Like any production LLM, MiniMax M2.7 comes with tradeoffs worth planning for:

• Commercial licensing requirement: MiniMax M2.7 is released under a non-commercial license; commercial deployments require a separate agreement with MiniMax. Teams must confirm licensing terms and negotiate access before shipping MiniMax M2.7 in a production application

• Text-only modality: MiniMax M2.7 accepts only text input and produces text output. Any workflow involving images or other non-text modalities requires routing to a separate model, adding integration complexity and routing logic

• Provider dependency: Relying on MiniMax as a single provider creates fragility when the provider experiences an outage or deprecates a model version. MiniMax's infrastructure footprint is smaller than that of hyperscaler-backed providers, and disruptions are harder to route around without a pre-configured fallback

• Cost at scale: At $1.20 per 1M output tokens, output costs compound at high request volumes. During benchmark evaluation, MiniMax M2.7 generated 87M output tokens, indicating verbose generation behavior that can increase per-request costs without explicit output length constraints

• Limited benchmark transparency: Specific MMLU, HumanEval, GSM8K, or Arena Elo scores for MiniMax M2.7 are not widely published, making it difficult to predict task-level performance before running in-house evals against the models you are currently using

Why should I use Merge Gateway to route LLM requests with MiniMax M2.7 and every other model?

Using MiniMax M2.7 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• Intelligent routing and automatic failover: Merge routes around MiniMax outages automatically. Because MiniMax M2.7's commercial licensing makes replacing it with an exact equivalent difficult, having automatic failover to a pre-configured fallback is especially important for maintaining uptime

• One API, every provider: Access MiniMax M2.7 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, with no application code changes required

• Cost governance: Set hard or soft project budgets so MiniMax M2.7 spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches MiniMax. Enforce per-project model and region policies without adding that logic to your application

How can I start using Merge Gateway to route requests with MiniMax M2.7?

Getting MiniMax M2.7 running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For MiniMax M2.7, the model string is minimax/minimax-m2.7. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming MiniMax M2.7 as primary with MiniMax M2.5 as a speed-optimized fallback for latency-sensitive requests.

Full setup instructions and SDK references are in the Merge Gateway docs.