MiniMax M2.7
minimaxai/minimax-m2.7
end-to-end software engineering agent.
cmd --model minimaxai/minimax-m2.7
Intelligence index
38.9
Output speed
not yet scored
Input
$0.30 /M
Output
$1.20 /M
Cache read
$0.06 /M
Agent-loop cost
$0.13 /M in
Context window
200K tokens
Released
March 18, 2026
Modalities
→
vs. the lineup
MiniMax M2.7 beside its stablemates and nearest rivals. The ◆ marks the best value in each column across every row shown.
pin a rival:
| Model | Intelligence | Coding | Speed | Input $/M | Output $/M | Blended $/M | Context |
|---|---|---|---|---|---|---|---|
| Muse Spark 1.2 Contributor | 56.8◆ | 72.2◆ | —◆ | $0.10◆ | $0.20◆ | $0.13◆ | 1.05M◆ |
| MiniMax M3 | 45.4◆ | 58.6◆ | 96.8◆ | $0.30◆ | $1.20◆ | $0.52◆ | 1M◆ |
| Qwen 3.7 Plus | 39.4◆ | 55.9◆ | 54.9◆ | $0.40◆ | $1.60◆ | $0.70◆ | 1M◆ |
| MiniMax M2.7 ◆ | 38.9◆ | 52.6◆ | —◆ | $0.30◆ | $1.20◆ | $0.52◆ | 200K◆ |
| MiniMax M2.5 | 34.5◆ | —◆ | —◆ | $0.30◆ | $1.20◆ | $0.52◆ | 200K◆ |
| Step 3.7 Flash | 30.9◆ | 39.6◆ | 391.3◆ | $0.20◆ | $1.15◆ | $0.44◆ | 256K◆ |
| DeepSeek V4 Pro (latest)pinned | 45.3◆ | 59.4◆ | 63.3◆ | $0.66◆ | $1.98◆ | $0.99◆ | 1M◆ |
| DeepSeek V4 Flash (latest)pinned | 52◆ | 69.1◆ | 115.9◆ | $0.22◆ | $0.66◆ | $0.33◆ | 1M◆ |
coding performance
The Intelligence Index and its sub-scores, ranked against every scored model in the catalog. A metric that has not been measured for MiniMax M2.7 has been left empty.
Coding Index
52.6
#34 of 40 scored
Terminal-Bench
55.4
#33 of 40 scored
Intelligence Index
38.9
#37 of 46 scored
Long-context reasoning
75.3
reasoning across a long context
SciCode
47
scientific coding
GPQA Diamond
87.4
graduate-level QA
usage calculator
How far a month of credits goes on MiniMax M2.7.
Input tokensfresh prompt
800Output tokensmodel reply
180Cache read tokensre-read context
50Kcost / request $0.0035 · in $0.30 · out $1.20 · cache $0.06 per M
fresh input 7%output 6%cache reads 87%
Requests / 30 days
2.9K
$10 credits ÷ $0.0035 per request
~579 quick fixes~116 bug fixes~19 feature PRs
what real work costs
Real coding tasks priced end to end on MiniMax M2.7, from a quick lookup to a full-repo agent run.
One agent task
$0.04
180K in at 75% cache hit, 12K out
What you pay
$0.19 /M
all-in across every token that task touched
Sticker input
$0.30 /M
cache reads bill at $0.06 /M instead
| Task | Tokens in · out | MiniMax M2.7 | Muse Spark 1.2 Contributor | Claude Haiku 4.5 |
|---|---|---|---|---|
| Quick lookup / one-liner | 8K · 1K | $0.0019 | $0.0003 | $0.0065 |
| Review a 500-line PR | 60K · 4K | $0.01 | $0.0027 | $0.04 |
| Fix a bug (agent loop) | 180K · 12K | $0.04 | $0.0072 | $0.12 |
| Refactor a module | 320K · 20K | $0.06 | $0.01 | $0.21 |
| Full-repo agent run | 900K · 45K | $0.16 | $0.03 | $0.49 |
frequently asked
What is MiniMax M2.7 best for?
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent collaboration, enabling it to plan, execute, and refine complex tasks across dynamic environments. Trained for production-grade performance, M2.7 handles workflows such as live debugging, root cause analysis, financial modeling, and full document generation across Word, Excel, and PowerPoint. It delivers strong results on benchmarks including 56.2% on SWE-Pro and 57.0% on Terminal Bench 2, while achieving a 1495 ELO on GDPval-AA, setting a new standard for multi-agent systems operating in real-world digital workflows.
How much does MiniMax M2.7 cost?
$0.30/M input and $1.20/M output, cache reads $0.06/M. In an agent loop most input is cache-read, so the effective input rate is about $0.13/M.Which plan do I need?
Available on Go and above.
How do I switch to it?
Run
cmd --model minimaxai/minimax-m2.7, or type /model in a session and pick it. You can switch mid-session without losing context.Ship code that matches your taste
Command Code is the AI coding agent that continuously learns your taste. Start for $1.
Benchmarks from Artificial Analysis (v4.1)commandcode.ai/models/minimax-m2-7