# Atlas with MiniMax-M2.7 in 2026: Agentic Reasoning at $0.30

> MiniMax-M2.7 is a 230B-parameter agentic MoE with a 204,800 token context at $0.30 per Mtok input and $1.20 per Mtok output, released March 2026.

MiniMax-M2.7 is MiniMax's March 2026 agentic model, a 230B-parameter MoE that NVIDIA has published tuning guidance for on its own infrastructure. Inside Atlas, MiniMax-M2.7 is built for exactly the long-horizon tool-calling shape Atlas produces: plan agent, then build agent, then parallel subagents. MiniMax-M2.7 still bills at $0.30 per Mtok input and $1.20 per Mtok output with a 204,800 token context, which keeps it among the cheapest current-generation reasoning models anywhere.

## Key takeaways

- MiniMax-M2.7 costs $0.30 per Mtok input and $1.20 per Mtok output with a 204,800 token context.
- MiniMax-M2.7 is 4.7x cheaper on input than GLM-5.1's $1.40 at a comparable context window.
- MiniMax-M2.7 is a 230B-parameter MoE tuned for agentic workflows, with published NVIDIA deployment guidance.
- MiniMax-M2.7's 131,072 max output with reasoning suits long toolchains across shell, retrieval, and code runners.
- MiniMax-M3 matches the $0.30 input price with a 1,000,000 token context, so M2.7 wins only on checkpoint maturity.

## Why is MiniMax-M2.7 built for long-horizon agent runs?

MiniMax-M2.7 is a 230B-parameter MoE tuned for scalable agentic workflows, with published deployment guidance from NVIDIA for running it on their platforms. Its 131,072 max output plus reasoning is sized for long-horizon toolchains across shell, retrieval, and code runners, which maps directly onto Atlas's tool set.

Atlas gives a model a lot of rope. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, then fans out work to subagents that can run in the foreground or in parallel background sessions, all while every tool call is permission-gated against allow, ask, and deny rules. A model that loses the thread after a dozen tool calls makes that architecture useless. MiniMax-M2.7's training target is precisely the multi-hour, many-tool trajectory, so it holds its plan across a shell command, a retrieval pass, and a test run without needing you to restate the goal. Enable the plan agent, then the build agent, then parallel subagents. MiniMax-M2.7 is trained for exactly that shape.

## How cheap is MiniMax-M2.7 compared to frontier models?

MiniMax-M2.7 charges $0.30 per Mtok input with a 204,800 token window, which is 4.7 times cheaper on input than GLM-5.1's $1.40 at a comparable context. On output, MiniMax-M2.7's $1.20 per Mtok keeps a reasoning-heavy Atlas session affordable in a way that frontier pricing does not.

The 4.7x input gap is what changes behavior rather than just the invoice. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and on a $1.40 input model you instinctively narrow that search to protect the budget. On MiniMax-M2.7 you do not have to. Feed the model the whole neighborhood of a change and let its reasoning do the filtering. The same logic applies to Atlas's parallel background subagents: at $0.30 per Mtok input and $1.20 per Mtok output, running several in parallel is a normal thing to do rather than a decision you have to defend.

## What are the real tradeoffs of MiniMax-M2.7?

MiniMax-M3 offers a 1,000,000 token context at the same $0.30 per Mtok input, so MiniMax-M2.7 only wins if you value the smaller, more mature checkpoint over raw window size. MiniMax-M2.7's 204,800 tokens is the constraint you are accepting in exchange for that maturity.

The second tradeoff is jurisdictional and technical at once. MiniMax-M2.7 is hosted in China behind an Anthropic-compatible shim, which is both a residency caveat and a compatibility caveat. The residency half is a policy question your organization answers, not one a config file can solve. The compatibility half is smaller in practice: Atlas connects fine, but the shim is not real Anthropic, so treat unexpected tool-streaming behavior as a compatibility gap before you blame MiniMax-M2.7's reasoning. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, which at least keeps the index itself out of the picture.

## MiniMax-M2.7 vs MiniMax-M3: which should Atlas use?

MiniMax-M2.7 and MiniMax-M3 both cost $0.30 per Mtok input and $1.20 per Mtok output. MiniMax-M2.7 gives Atlas 204,800 tokens of context and 131,072 max output; MiniMax-M3 gives 1,000,000 tokens of context. Since price is identical, the decision is entirely about window size and checkpoint maturity.

Pick MiniMax-M2.7 when your work is scoped to a service and its tests, which is most agent work, and when you want the checkpoint that NVIDIA has published deployment guidance for. A 204,800 token window filled with tree-sitter AST declarations rather than blind line windows goes a long way. Pick MiniMax-M3 when a task genuinely needs to see a whole repository at once and no amount of retrieval tuning gets you there. Atlas lets you switch the active model and provider on the fly with favorites and recents, so favorite both and move between them per task rather than committing to one forever.

## How do you get the most out of MiniMax-M2.7 in the Atlas TUI?

Pin `"model": "minimax/MiniMax-M2.7"` in atlas.json, then enable Atlas's plan agent, then build agent, then parallel subagents. MiniMax-M2.7 is trained for exactly this long-horizon tool-calling shape, and the 131,072 output cap gives it room to write a full plan and a full implementation.

Two habits pay off with MiniMax-M2.7 specifically. First, let the plan agent finish. A read-only planning pass is the cheapest possible way to catch a bad approach, and on a model with reasoning enabled at $1.20 per Mtok output it costs very little to let the plan be thorough. Second, review the diff. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and an agentic model that is comfortable making many tool calls in sequence is exactly the kind you want to keep behind that gate. Atlas also snapshots file changes as git patches so edits can be diffed and rolled back.

## Setup

1. Export MINIMAX_API_KEY or run `atlas login` and choose MiniMax.
2. Run `atlas models minimax` and confirm `MiniMax-M2.7` resolves in the registry.
3. Pin `"model": "minimax/MiniMax-M2.7"` in atlas.json.
4. Enable Atlas's plan agent, then build agent, then parallel subagents. MiniMax-M2.7 is trained for exactly this long-horizon tool-calling shape.
5. Keep the allow, ask, and deny permission rules in place, since an agentic model issues many tool calls in sequence.

## FAQ

### how much does minimax-m2.7 cost per million tokens

MiniMax-M2.7 costs $0.30 per Mtok input and $1.20 per Mtok output, which makes it one of the cheapest current-generation reasoning models available to a coding agent.

### what is the minimax-m2.7 context window

MiniMax-M2.7 has a 204,800 token context window and a 131,072 max output cap with reasoning enabled.

### is minimax-m2.7 good for agentic coding workflows

MiniMax-M2.7 is a 230B-parameter MoE tuned for scalable agentic workflows, sized for long-horizon toolchains across shell, retrieval, and code runners, which maps directly onto Atlas's tool set.

### minimax-m2.7 vs glm-5.1 cost

MiniMax-M2.7's $0.30 per Mtok input is 4.7x cheaper than GLM-5.1's $1.40 at a comparable context window, and MiniMax-M2.7's $1.20 output undercuts GLM-5.1's $4.40.

### how do i configure minimax-m2.7 in atlas

Export MINIMAX_API_KEY or run `atlas login` and choose MiniMax, run `atlas models minimax` to confirm `MiniMax-M2.7` resolves, then pin `"model": "minimax/MiniMax-M2.7"` in atlas.json.

### should i use minimax-m2.7 or minimax-m3

MiniMax-M3 offers a 1,000,000 token context at the same $0.30 per Mtok input, so MiniMax-M2.7 only wins if you value the smaller, more mature checkpoint over the larger window.

### where is minimax-m2.7 hosted

MiniMax-M2.7 is hosted in China behind an Anthropic-compatible shim, which is both a data residency caveat and a compatibility caveat for teams evaluating it.

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