MiniMax M2.5: Coding Benchmarks

I've been watching this particular matchup build for weeks. On February 12, 2026 — barely a month after MiniMax's Hong Kong IPO — they dropped M2.5, and my Slack immediately lit up with the same message from five different engineers: "Have you seen these numbers?"

Here's the thing that actually got me: MiniMax M2.5 scores 80.2% on SWE-Bench Verified. Claude Opus 4.6, released just a week earlier on February 5, sits at 80.8%. That's a 0.6 percentage point gap between a model that costs roughly $0.15/M input tokens and one that costs $5/M input tokens. I've been running both through real production scenarios over the past week, and this article is what I actually found — not just what the benchmarks say.

If you're a developer, tech lead, or engineering manager trying to figure out how to route your coding tasks in 2026, this is the comparison you actually need.

What This Comparison Is Actually For — Stop Arguing, Start Shipping

Look, I've been in software engineering for over a decade, and I've never seen a benchmark debate waste more developer hours than the "which model is better" argument. The real question isn't which model wins — it's which model should handle which task in your stack, at what cost.

This comparison isn't for academic purposes. It's built around a specific decision problem: you're building or running an AI-assisted development workflow, you have access to both models, and you need a routing strategy.

The models we're comparing:

• MiniMax M2.5 — Released February 12, 2026. A 230B-parameter Mixture-of-Experts model that activates only 10B parameters per forward pass. Trained across 200,000+ real-world RL environments, open-sourced on Hugging Face.
• Claude Opus 4.6 — Released February 5, 2026. Anthropic's most capable model, featuring Adaptive Thinking, a 1M token context window (beta), and industry-leading Terminal-Bench 2.0 performance.

Both are production-ready. Both are genuinely good. But they're not interchangeable.

Benchmarks Snapshot (Verified Feb 2026)

Let me pull the numbers together cleanly. All figures below are from official model releases and verified third-party evaluations as of February 2026.

*Sources: MiniMax official announcement, Anthropic official page, SWE-bench Results Viewer, Artificial Analysis.*

SWE-Bench + What the 0.6% Gap Actually Means in Production

Here's where I need to pump the brakes on the headline narrative. SWE-Bench Verified tests autonomous resolution of real GitHub issues in Python repositories — it's the best single benchmark we have for coding agents. And yes, 80.2% vs 80.8% is statistically close.

But the type of tasks each model handles better matters enormously. The gaps I observed in practice were larger than 0.6% would suggest:

• Multi-SWE-Bench tests multi-file, multilingual projects — M2.5 actually leads here at 51.3% vs 50.3%. For Go, Rust, TypeScript, and Java work, this is the more relevant benchmark.
• BFCL Multi-Turn is the one that really jumped out at me. A 13.5 percentage point lead for M2.5 in sustained multi-turn tool calling means it loses context far less often in long agentic loops. In a Verdent multi-agent workflow where you're running a refactor agent for 40+ tool calls, that difference is enormous.
• Terminal-Bench 2.0 tells the opposite story. Opus 4.6's 65.4% vs M2.5's 52% reflects a real gap in autonomous terminal operations, multi-step debugging, and OS-level tasks. If your agent needs to navigate a filesystem, run test suites iteratively, or debug at the shell level, Opus 4.6 earns its premium.

Bottom line: 0.6% on SWE-Bench masks a bifurcated performance profile. M2.5 dominates tool-calling loops; Opus 4.6 dominates terminal and reasoning-heavy tasks.

Our Task-Based Test Results

I ran both models through a set of real tasks over 7 days — not toy problems, but representative work from actual project types: a multi-service API refactor, a business logic validation suite, some niche language work in Rust, and a TypeScript component library migration.

M2.5 Wins — Multi-File Refactors, Sustained Tool-Calling Loops

Task: Migrate a 14-file Express.js API from callbacks to async/await, with full test suite updates

M2.5's native "spec-writing behavior" — where it plans architecture before touching code — made a real difference here. Before writing a single line, it produced a structured migration plan covering file dependencies, shared callback patterns, and test impact surface. The final output required about 20% less manual review than Opus 4.6's equivalent attempt.

The BFCL multi-turn advantage showed up clearly in the tool-calling loop. M2.5 maintained accurate context across 47 consecutive tool calls. Opus 4.6 showed context drift around call 28, requiring a manual injection to get it back on track.

# Example: M2.5 parallel tool-calling pattern in Verdent Agent Mode
# M2.5 successfully ran these three tool calls in parallel without context loss:
# 1. Read src/routes/users.js → analyze callback chains
# 2. Read src/middleware/auth.js → identify callback dependencies  
# 3. Read tests/users.test.js → map test coverage to callbacks
# Total rounds needed: 6
# Opus 4.6 equivalent: 9 rounds (context drift at round 5)

Other M2.5 wins in our testing:

• Multi-language refactors (TypeScript + Go in the same session)
• Long-running Verdent Agent sessions (30+ tool calls)
• Rust trait implementations with complex lifetimes (multilingual training advantage)
• Cost-optimized batch processing of code review tasks

Opus 4.6 Wins — Tricky Business Logic, Niche Language Edge Cases

Task: Implement a multi-currency rounding engine with regulatory compliance rules across 6 jurisdictions

This is exactly the kind of task where Opus 4.6's gap in reasoning and planning precision shows up. The business logic was ambiguous in three places where different jurisdictions contradict each other. Opus 4.6 flagged all three conflicts proactively and requested clarification before proceeding. M2.5 made assumptions on two of them — one was correct, one was not.

For tasks that touch regulatory logic, financial calculations, or require deep domain reasoning, Opus 4.6's Adaptive Thinking and higher AIME scores represent real production value.

Other Opus 4.6 wins in our testing:

• Autonomous debugging sessions requiring shell-level operations (Terminal-Bench gap is real)
• Obscure language edge cases in Python's typing module, C++ template metaprogramming
• Tasks requiring 200K+ token context with full coherence
• Novel architecture design where first-principles reasoning matters

Cost Reality — What Your Monthly Bill Actually Looks Like

Let me be direct about the numbers, because this is where the decision often ends.

*Pricing from MiniMax official announcement and Anthropic's API pricing page, February 2026.*

Here's the math that matters for teams running coding agents at scale:

Scenario: 100 code review** tasks/day, ~1M output tokens total**

• M2.5 Standard: ~$1.20/day → $36/month
• Opus 4.6 Standard: ~$25.00/day → $750/month

That's a 20x cost difference on output tokens. For a mid-size engineering team running agents daily, routing even 70% of tasks to M2.5 generates significant savings with near-equivalent output quality on most task types.

Two Opus 4.6 cost levers worth knowing: Batch API** gives 50% off**, and prompt caching drops input to $0.50/M tokens (90% savings). If you're running repeated system prompts or document-heavy workflows, stack both. But even with caching, M2.5 remains meaningfully cheaper for output-heavy agentic workloads.

Decision Matrix + How Verdent Routes in Production

After a week of real-world testing, here's the routing framework I'd actually implement. At Verdent, we route between models based on task type, risk level, and cost tolerance — not a single "best model" assumption.

The routing logic in plain English:

Use M2.5 as your default agent model for iterative, tool-heavy, multi-file coding work. Route to Opus 4.6 when the task requires deep reasoning, autonomous terminal navigation, or when a mistake has significant downstream consequences.

This isn't a permanent hierarchy — it's a cost-risk tradeoff. The 0.6% SWE-Bench gap doesn't define which model is "better." It tells you they're close enough that task type and cost should drive routing, not model prestige.

A quick note on Verdent's implementation: In Verdent's multi-agent architecture, the Plan Mode agent decides routing based on task classification. Git Worktree isolation means each agent — whether running M2.5 or Opus 4.6 — works in a sandboxed environment, so routing decisions don't create code conflicts. If you're implementing something similar, that isolation layer matters more than model selection for maintaining code safety.

FAQ

Q: Is MiniMax M2.5 actually open source? A: Yes — MiniMax released the weights publicly. The MiniMax M2.5 model card and weights are available on Hugging Face, with vLLM and SGLang support for self-hosting. Note that with 230B total parameters, self-hosting requires serious hardware even with MoE's 10B active parameter footprint.

Q: Which model should I use for a greenfield project from scratch? A: For 0-to-1 system design, I'd lean Opus 4.6. Its reasoning depth and Adaptive Thinking give it an edge when the architecture isn't defined yet. Once you're in 1-to-N feature development with a defined codebase, M2.5 becomes competitive or better.

Q: Does the BFCL multi-turn gap matter for me? A: It matters if your agent runs more than ~20 consecutive tool calls in a session. Below that threshold, both models maintain context well. Above it — especially in refactors spanning many files — M2.5's 76.8% vs 63.3% on the Berkeley Function Calling Leaderboard multi-turn benchmark translates to fewer context drift failures.

Q: What about Claude Sonnet 4.6 as an alternative? A: Worth mentioning. Sonnet 4.6 scores 79.6% on SWE-Bench Verified at $3/$15 per million tokens — that's closer to M2.5's price and nearly Opus 4.6's benchmark score. For Anthropic-native stacks, Sonnet 4.6 is the pragmatic middle ground.

Q: How does M2.5's 37% faster task completion actually affect cost? A: Good question. Because M2.5 also consumes slightly fewer tokens per task (3.52M vs M2.1's 3.72M average on SWE-Bench), the cost advantage compounds — you're paying less per token and using fewer tokens per task. On continuous operation, MiniMax prices M2.5 Lightning at $1/hour at 100 TPS. That's an unusual way to think about model pricing, but useful for long-running agent sessions.

What's the Bottom Line?

Stop debating models. Start routing tasks.

The MiniMax M2.5 vs Claude Opus 4.6 decision isn't binary. It's a routing problem: M2.5 for iterative, multi-file, tool-heavy work at scale; Opus 4.6 for reasoning-intensive, terminal-heavy, or high-stakes tasks where the cost premium buys you meaningful precision.

Use the decision matrix above to route your coding tasks by task type, risk level, and cost. If you're running a Verdent-style multi-agent stack, implement model routing at the plan layer so individual agents don't inherit a one-size-fits-all model assumption. That single architectural decision will do more for your team's output quality and infrastructure costs than any single model choice.

Data Sources: MiniMax official release (Feb 12, 2026), Anthropic official model page (Feb 5, 2026), Anthropic API pricing docs (Feb 2026), SWE-bench Results Viewer (Feb 17, 2026), Artificial Analysis benchmark data, HuggingFace M2.5 analysis (Feb 2026).