A comprehensively upgraded general-purpose model called MiniMax M2.5, announced by MiniMax and positioned as a model built specifically for agentic workflows, code generation, and “real-world productivity.” The company describes M2.5 as the result of extensive reinforcement-learning training in hundreds of thousands of complex environments, delivering major gains in coding benchmarks, tool use, and long-context reasoning while pushing inference efficiency and cost effectiveness.
You can already see MiniMax M2.5 on CometAPI. Its price is 20% of the official price in CometAPI.
What is MiniMax M2.5 and why does it matter?
MiniMax M2.5 is the newest major release from MiniMax, a model family positioned around high-throughput, agentic workflows and — above all — coding productivity. Announced in mid-February 2026, M2.5 extends the company’s previous M-series work with a larger context window, tighter tool-integration primitives, and a training emphasis on “AI-native workspaces” where the model actively orchestrates browser searches, API calls, and code execution steps rather than just returning text. The launch messaging frames M2.5 not as a general conversational upgrade alone, but as a platform-level move: it’s intended to accelerate developer productivity, automate repetitive engineering tasks, and serve as an engine for agent-driven products.
Why this matters today is two-fold. First, the model hits a set of practical benchmarks and throughput goals that make it attractive for production systems (not just research demos). Second, the release signals how vendors are prioritizing integrated tool use and token efficiency: M2.5 is explicitly tuned to reduce the number of tool-call rounds and token churn during multi-step tasks, which translates directly to lower cost and latency in real-world deployments.
How Does MiniMax M2.5 Perform in Coding Benchmarks?
Overview of Coding Performance
MiniMax M2.5 has quickly gained attention for its performance on standard coding benchmarks used across the AI industry to evaluate practical code generation and reasoning:
| Benchmark Suite | M2.5 Result | Explanation |
|---|---|---|
| SWE-Bench Verified | 80.2% | Measures ability to fix real GitHub issues; near top performance. |
| Multi-SWE-Bench | 51.3% | Evaluates multi-file, cross-repository coding reliability. |
| SWE-Bench Pro | 55.4% | Harder real-world coding test. |
Benchmarking data suggests that M2.5’s coding prowess matches highly-ranked proprietary models such as Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.2, placing M2.5 among the top contenders for production software engineering tasks. Scoring above 80% in this benchmark signals that M2.5 is capable of practical software engineering assistance, not just theoretical code generation. This makes it especially valuable for enterprise workflows where correctness, reliability, and maintainability are top-tier priorities.
These figures show M2.5 operating at industry-leading levels without the extreme pricing burden typical of many closed proprietary systems — a point that directly challenges recent industry perceptions that high performance necessarily correlates with high cost.
How does M2.5 behave on real engineering workflows?
Beyond raw scores, what’s noteworthy is how M2.5 is architected for agentic pipelines. The model includes primitives for interleaved thinking (internal deliberation between tool calls), stronger multi-turn code reasoning, and a context-management strategy for long codebases. In early tests, reviewers reported that M2.5 generated a large share of commit-ready code for certain classes of tasks and required fewer human corrections than earlier MiniMax versions. That combination — stronger first-pass correctness and fewer back-and-forth cycles — is what makes M2.5 attractive for code-assist and CI automation roles.
Search and Tool calling of MiniMax M2.5
Although coding performance is often a central metric for developer-oriented LLMs, M2.5 is designed for broader productivity:
| Task Type | Benchmark | M2.5 Score |
|---|---|---|
| Web Search & Context | BrowseComp | 76.3% |
| Tool-Use Reasoning | BFCL Multi-Turn | 76.8% |
| Workflow Orchestration | MEWC (Multi-Expert) | 74.4% |
| Office Productivity | VIBE-Pro Suite | 54.2% |
These metrics highlight that M2.5’s capabilities extend into dense, multi-step reasoning, effective search within stored context, and long-horizon tool interactions — key competencies for robust multi-modal AI assistants and agents.
Can it find and use tools effectively?
One of the headline improvements in M2.5 is tool integration. The model’s internal “interleaved thinking” capability enables it to reflect before and after each tool call, decide whether it needs another search or a different tool, and synthesize disparate tool outputs into a coherent next step. Practically, this reduces the number of tool-call rounds required to solve a multi-step task (search → fetch → analyze → act). Platform documentation and hands-on reviews report roughly 20% fewer tool-call rounds and a significant increase in “decision maturity,” meaning the model makes fewer redundant or premature tool calls.
Benchmarks that focus on browsing and tool workflows (BrowseComp, BFCL) place M2.5 near the top of the pack for agentic tasks. BrowseComp scores in the mid-70s were reported, and BFCL-style tool calling tests show high precision in multi-step tool orchestration. Those results matter for any product that expects a model to synthesize live web data, call domain-specific APIs, or actively manipulate files and code on a user’s behalf.
What does this mean for integrations?
For engineers building assistants, bots, or automation pipelines, the takeaway is that M2.5 is not just “better at searches” — it’s better at decision-making about searches. That means fewer round trips, less token waste, and simpler orchestration code in many cases.
What Are MiniMax M2.5’s Efficiency and Speed Characteristics?
One of M2.5’s headline attributes is its speed and inference efficiency — a critical consideration for real-world usage where throughput affects both cost and latency.
Efficiency Metrics
| Metric | Value |
|---|---|
| Speed Improvement vs M2.1 | +37% |
| Standard Output Speed | 50 tokens/second |
| Lightning Output Speed | 100 tokens/second |
| Typical Tokens/Task | ~3.52M tokens for complex tasks |
The Lightning variant matches the throughput of models like Claude Opus 4.6 — but crucially at a fraction of the cost. This allows M2.5 to support continuous agentic workflows without prohibitive token expenses over long sessions or high volume operational use.
Engineering Implications
- Higher throughput directly correlates with faster real-time interaction in development loops and automated workflows.
- Better token efficiency reduces total cost in long form, multi-stage tasks like documentation generation, debugging, and cross-system integration.
- Combined with M2.5’s high reasoning benchmarks, this efficiency means better results at a lower total runtime cost compared to competing frontier models.
What Does MiniMax M2.5 Cost? — Pricing Breakdown
One of the most disruptive aspects of M2.5 is its pricing — positioned as a cost-efficient alternative to proprietary LLMs. What pricing options does MiniMax offer?
MiniMax provides a few different consumption and subscription options targeted at developers and businesses. The company’s public materials outline two billing approaches for text models in production: a Coding Plan subscription (aimed at developers who run a steady volume of code-related prompts) and Pay-As-You-Go for flexible, metered usage. The Coding Plan is explicitly designed to present an inexpensive monthly option for developer teams, while the pay-as-you-go route charges by token or by the selected throughput profile.
How does the Coding Plan work?
The Coding Plan is pitched as a monthly subscription that bundles a fixed number of “prompts” or sessions over a time slice (examples in documentation include tiers like starter/plus/max with different prompt allowances every 5 hours). The stated rationale is to offer a predictable, developer-friendly cost structure for teams that rely on many short, frequent code-assist sessions rather than high-volume single requests.
| Starter | Plus | Max | |
|---|---|---|---|
| Price | $10 /month | $20 /month | $50 /month |
| Prompts | 100 prompts / 5 hours | 300 prompts / 5 hours | 1000 prompts / 5 hours |
| Starter | Plus | Max | |
|---|---|---|---|
| Price | $100 /year 120 | $200 /year 240 | $500 /year 600 |
| Prompts | 100 prompts / 5 hours | 300 prompts / 5 hours | 1000 prompts / 5 hours |
Token Pricing Structure
| Variant | Input Price | Output Price | TPS (Tokens/sec) | Notes |
|---|---|---|---|---|
| M2.5-Standard | $0.15/M | $1.20/M | 50 | Cost-optimized variant. |
| M2.5-Lightning | $0.30/M | $2.40/M | 100 | Speed-optimized variant. |
These token price rates effectively democratize AI agent economics, allowing models to run continuously at enterprise scales without cost barriers faced by many proprietary systems that price output tokens 10×–30× higher.
Hourly Operational Cost
Using the Lightning variant (100 TPS), stable continuous output results in roughly:
- 360,000 tokens generated per hour
- Output cost = 360,000/1M × $2.40 ≈ $0.86
- Input cost adds a fraction more for ~$1/hour total continuous output cost
This is orders of magnitude cheaper than typical frontier models, making always-on agentic operations economically viable for businesses.
Looking for a cheaper way to use M2.5
Enjoying a discount of Minimax-M2.5 when using CometAPI:
| Comet Price (USD / M Tokens) | Official Price (USD / M Tokens) | Discount |
|---|---|---|
| Input:$0.24/M; Output:$0.96/M | Input:$0.3/M; Output:$1.2/M | -20% |
How do you get started with MiniMax M2.5
Where can developers access the model?
MiniMax publishes documentation and platform guides for integrating M2.5 via its API (platform docs include guides for text, coding, and tool-driven flows). The model is also in some third-party model libraries and registries (for example, several platform libraries surfaced M2.5 variants for cloud usage and for local experimentation). That means developers can either call M2.5 through MiniMax’s official API endpoints or use supported third-party tooling where available.
Common integration patterns
- IDE / Editor assistant — hook M2.5 into an IDE plugin to provide completions, explainers, and test-case generation. Use a ‘Coding Plan’ subscription if you expect many short developer sessions.
- Agent orchestration — embed M2.5 as the decision brain in a multi-tool orchestration system; rely on its strong tool-calling behavior to manage external actions (APIs, database queries, test runners). Ensure explicit schema contracts for API payloads to minimize hallucinations.
- Search + retrieval augmentation — combine a small retrieval layer (vector store + reranker) to limit context token usage while preserving relevance for long-doc queries. M2.5’s strong search-bench performance makes it a natural fit for retrieval-augmented generation.
- Batch code transformation — leverage the model for bulk refactors or automated test generation by running batched jobs, where cost per hour and throughput settings are particularly important to model economics.
Practical tips for better results
- Use few-shot examples that mirror the developer flow (input, desired output shape, failure cases) to improve correctness for coding or tool invocation prompts.
- Lock down tool interfaces with schema validation so that when M2.5 issues an API call the system accepts only validated payloads.
- Monitor token usage and set safeguards (hard token limits per call) to avoid runaway bills.
- Measure success rates (e.g., test pass rate for generated code) rather than purely relying on subjective quality metrics.
Conclusion
MiniMax M2.5 represents a pragmatic step forward in the “agent + coding” niche of large models: it combines strong coding benchmarks, explicit support for interleaved tool use, and operational improvements aimed at reducing token and time costs in real workflows. For teams focused on developer productivity automation, code generation, and multi-tool orchestration, M2.5 is worth piloting — especially where cost-efficiency is a priority. For teams requiring the absolute bleeding-edge in every niche benchmark irrespective of cost, premium offerings may still show incremental advantages; but the cost/perf tradeoffs make M2.5 compelling for production deployment in many real-world scenarios.
Developers can access MInimax-M2.5 via CometAPI now.To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.
Ready to Go?→ Sign up fo glm-5 today !
If you want to know more tips, guides and news on AI follow us on VK, X and Discord!