---
title: "MiniMax M2.5: The New Coding Champion Costs 90% Less Than You'd Expect"
description: "Benchmark-topping coding performance, agent-native design, and pricing that makes Claude look expensive. We break down why M2.5 matters for anyone building AI products."
date: 2026-02-13
location: San Francisco, CA – Feb 13th, 2026
image: /images/minimax-m2-1-what-developers-are-doing-hero.png
faq:
  - question: "How does MiniMax M2.5 compare to Claude Opus on coding benchmarks?"
    answer: "MiniMax M2.5 beats Claude Opus 4.6 on SWE-bench Pro and SWE-bench Verified — the benchmarks that test real-world software engineering with actual GitHub issues. It also runs ~3x faster (~100 TPS vs ~30 TPS) and costs 10-20x less per token."
  - question: "What does MiniMax M2.5 cost per token?"
    answer: "MiniMax M2.5 costs $0.30/M input tokens and $1.20/M output tokens, with native prompt caching at $0.06/M for cache reads. Compare that to Claude Opus 4 at ~$15/$75 per million tokens — M2.5 is roughly 50x cheaper on input and 60x cheaper on output."
  - question: "Is MiniMax M2.5 good for AI agents?"
    answer: "Yes. M2.5 was designed from the ground up for autonomous AI systems with reliable multi-step tool execution, long-horizon planning, and multilingual programming. Its ~100 TPS output speed means agent workflows with dozens of tool calls complete significantly faster than with Opus-class models."
---

MiniMax just dropped M2.5, and the numbers are weird.

This model beats Claude Opus 4.6 on SWE-bench Pro and SWE-bench Verified. It ships with native prompt caching at $0.06/M tokens for cache reads. And it costs roughly one-tenth to one-twentieth of comparable models on a per-token basis.

For developers building AI-powered products, this changes the calculus on every routing decision.

## The benchmarks that matter

SWE-bench isn't a toy benchmark. It tests real-world software engineering: take a GitHub issue, write the code to fix it, and get the PR merged. M2.5 doesn't just compete here—it leads.

Combined with SWE-bench Multilingual, SWE-bench-pro, and MultiSWE-bench, MiniMax is explicitly positioning M2.5 as the model for developers who ship code for a living.

The practical implication: if you're building coding assistants, automated PR review tools, or any dev-focused AI workflow, M2.5 belongs in your routing matrix.

## Built for agents, not just chat

This matters most for the agent use case. M2.5 was designed from the ground up for autonomous AI systems:

- **Tool-calling chains:** Reliable multi-step tool execution for complex workflows
- **Long-horizon planning:** Extended reasoning without losing the thread
- **Multilingual programming:** Code in any language without performance degradation

Most models do okay at single-turn tasks. Agents need models that maintain coherence across dozens of tool calls and maintain internal state. M2.5's architecture optimizes for exactly this.

## The economics stack up

Pricing is where this gets interesting for cost-conscious developers:

| Metric | MiniMax M2.5 | Claude Opus 4 | GPT-4o |
|--------|-------------|---------------|--------|
| Input tokens | $0.30/M | ~$15/M | $5/M |
| Output tokens | $1.20/M | ~$75/M | $15/M |
| Prompt cache reads | $0.06/M | N/A | N/A |
| Cache writes | $0.375/M | N/A | N/A |
| Output speed | ~100 TPS | ~30 TPS | ~60 TPS |

The raw token math is compelling. But for agent workloads, the speed matters more than the pricing. At roughly three times faster than Opus-class models, M2.5 compounds its advantage in any workflow requiring multiple model calls.

For a 100-step agent task that might hit 500 tokens through the model, the latency savings alone could be measured in seconds per task.

## Two access paths, same model

MiniMax ships M2.5 in two variants:

- **M2.5:** Standard version, full capability
- **M2.5-lightning:** Same results, optimized for speed

Both versions support automatic prompt caching with no configuration required. The model weights are fully open-sourced on HuggingFace, which means you can also self-host if you prefer not to use the API.

## Where this fits in your routing stack

For Haimaker users, M2.5 opens up new routing opportunities:

1. **Coding workloads:** Route complex code generation to M2.5 instead of Claude, save 80-90% on identical output quality
2. **Agent workflows:** M2.5's speed advantage compounds on multi-step tasks—more agent steps per second, lower latency end-to-end
3. **Caching optimization:** High-volume applications with repeated context benefit from native caching that other providers charge more for
4. **Fallback routing:** Add M2.5 as a cost-effective fallback when primary models hit rate limits

The combination of SOTA coding performance, agent-native design, and aggressive pricing makes M2.5 a first-tier option for production workloads—not just a "cheap alternative."

## The catch

Nothing is free. A few considerations:

- M2.5 is newer, which means less real-world testing in production environments
- The model weights are open source, but optimal deployment requires vLLM or SGLang infrastructure
- Benchmark performance doesn't always translate 1:1 to specific use cases

Start with low-volume routing, measure against your own quality bar, then scale up.

## How to try it

Haimaker already supports MiniMax M2.5 routing. Set up rules for your coding workloads, add it to your agent routing matrix, or use it as a fallback for high-volume production traffic.

The $10 in free credits cover enough tokens to run a solid benchmark against your current model mix and see where the savings show up.

Start routing at [haimaker.ai](https://haimaker.ai).