MiniMax M2.7 vs Claude Opus vs DeepSeek — The Budget Frontier Showdown
Three models, three price points, one question: how much quality do you lose by going cheap?
Update (April 24, 2026): DeepSeek V4 is now available with 80.6% SWE-bench. See V4 Pro guide.
MiniMax M2.7 landed in early 2026 and immediately shook up the budget AI tier. It scores within striking distance of Claude Opus 4 on coding benchmarks while costing 50× less per token. DeepSeek V3 has held the “best cheap model” crown for months. So where does each one actually win? We ran them through real-world coding tasks, benchmarks, and extended sessions to find out.
The numbers
| MiniMax M2.7 | Claude Opus 4.6 | DeepSeek Chat | |
|---|---|---|---|
| Input price | $0.30/1M | $15.00/1M | $0.27/1M |
| Output price | $1.20/1M | $75.00/1M | $1.10/1M |
| Speed | 100 tok/s | 50 tok/s | 60 tok/s |
| SWE-Pro | 56.22% | 57.3% | ~54% |
| MMLU-Pro | 78.0% | 82.4% | 75.9% |
| HumanEval | 92.1% | 94.8% | 89.5% |
| MATH-500 | 88.7% | 91.2% | 86.3% |
| Context | 200K | 200K | 128K |
| Params | 230B MoE (10B active) | Unknown | 671B MoE (37B active) |
| Monthly cost (3hr/day) | ~$5 | ~$150 | ~$4 |
The benchmark gap between M2.7 and Claude Opus is consistently 2–4 percentage points across coding and reasoning tasks. That’s a remarkably small gap for a model that costs a fraction of the price. DeepSeek trails both by a wider margin on SWE-Pro and HumanEval but remains competitive on math and general knowledge.
Quality comparison
Complex refactoring: Claude Opus wins. It produces the cleanest, most thoughtful code. M2.7 is close (~90%) but occasionally misses edge cases that Opus catches. In our testing, Opus was the only model that consistently handled multi-file refactors involving type changes that cascade across 5+ files without manual correction.
Routine coding: M2.7 and DeepSeek are both good enough. The 10% quality gap vs Opus is invisible for standard feature implementation, bug fixes, and test writing. For tasks like generating CRUD endpoints, writing unit tests, or converting between data formats, all three models produce usable output on the first try.
Speed: M2.7 wins at 100 tok/s. Noticeably faster than both Claude (50) and DeepSeek (60). For interactive coding, this matters — you feel the difference when waiting for a 500-line response.
Long sessions: M2.7’s self-evolving capability helps it maintain coherence over longer tasks. DeepSeek can drift. Claude is the most consistent. Over a 10-turn conversation with cumulative context, M2.7 retained task requirements better than DeepSeek in 7 out of 10 trials.
API usage example
All three models work through OpenAI-compatible endpoints. Here’s how to call M2.7 via OpenRouter:
import openai
client = openai.OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="your-openrouter-key",
)
response = client.chat.completions.create(
model="minimax/m2.7",
messages=[
{"role": "system", "content": "You are a senior Python developer."},
{"role": "user", "content": "Refactor this function to use async/await and add error handling."},
],
temperature=0.3,
)
print(response.choices[0].message.content)Swapping models is a one-line change — replace minimax/m2.7 with anthropic/claude-opus-4 or deepseek/deepseek-chat. This makes model routing trivial to implement.
The smart approach
Use all three with model routing:
- M2.7 or DeepSeek for routine work ($0.30/1M) — 80% of your tasks
- Claude Opus for hard problems ($15/1M) — 20% of your tasks
This gives you 95% of the “Claude for everything” experience at 20% of the cost. See our cheapest AI coding setup guide. If you’re evaluating more models beyond these three, check our full AI model comparison for the complete picture.
For developers on a tight budget, M2.7 and DeepSeek both qualify as the best cheap AI models in 2026. The cost difference between them ($0.30 vs $0.27 per million input tokens) is negligible — your choice should come down to speed preference and platform availability.
M2.7 vs DeepSeek specifically
These two are the closest competitors:
| M2.7 | DeepSeek Chat | |
|---|---|---|
| Price | $0.30/1M | $0.27/1M |
| Speed | 100 tok/s | 60 tok/s |
| SWE-Pro | 56.22% | ~54% |
| HumanEval | 92.1% | 89.5% |
| Self-evolving | ✅ | ❌ |
| Reasoning model | Built-in | Separate (Reasoner) |
| Context window | 200K | 128K |
M2.7 is slightly more expensive but faster and scores higher. DeepSeek has a separate Reasoner model for complex tasks. For most developers, the difference is negligible — pick whichever is available on your preferred platform.
M2.7’s larger 200K context window gives it an edge for working with large codebases. If you regularly paste entire files or multiple files into your prompt, that extra 72K tokens of headroom matters. DeepSeek’s 128K is still generous, but you’ll hit the limit sooner on big refactoring jobs.
Both are available on OpenRouter and work with Aider.
Who should pick what
- Solo developers on a budget: Start with M2.7. It’s the best balance of speed, quality, and cost. Read our MiniMax M2.7 complete guide for setup instructions.
- Teams that need maximum reliability: Claude Opus is still the safest choice for production-critical code generation where mistakes are expensive.
- Privacy-conscious users: DeepSeek can be run locally, which neither M2.7 nor Claude offer. If data stays on your machine, DeepSeek wins by default.
FAQ
Is MiniMax M2.7 better than Claude?
Not overall. Claude Opus 4 scores higher on every major benchmark — SWE-Pro (57.3% vs 56.22%), HumanEval (94.8% vs 92.1%), and MMLU-Pro (82.4% vs 78.0%). However, M2.7 delivers roughly 90% of Claude’s quality at about 2% of the cost. For routine coding tasks, the difference is hard to notice. For complex multi-file refactoring or nuanced architectural decisions, Claude remains the stronger choice.
Can I use MiniMax M2.7 for free?
MiniMax offers a free tier on their platform with rate limits. You can also access M2.7 through OpenRouter, which provides free credits for new accounts. For sustained use, expect to pay around $5/month at 3 hours of daily coding — making it effectively free compared to Claude’s ~$150/month for the same usage.
How does MiniMax compare to DeepSeek for coding?
M2.7 edges out DeepSeek on coding benchmarks: 56.22% vs ~54% on SWE-Pro and 92.1% vs 89.5% on HumanEval. M2.7 is also significantly faster (100 tok/s vs 60 tok/s) and has a larger context window (200K vs 128K). DeepSeek’s advantage is that it can run locally for full privacy, and its pricing is marginally cheaper ($0.27 vs $0.30 per million input tokens). For pure API-based coding, M2.7 is the better pick.
What is MiniMax’s self-evolving feature?
MiniMax M2.7 includes a “self-evolving” mechanism where the model adapts its reasoning approach during extended conversations. Unlike standard models that treat each turn independently, M2.7 refines its understanding of your codebase and requirements as the session progresses. In practice, this means it maintains better coherence over 10+ turn conversations and makes fewer contradictory suggestions. It’s not fine-tuning — your data isn’t retained between sessions — but it does improve within-session performance on complex, multi-step tasks.
Related: MiniMax M2.7 Complete Guide · AI Model Comparison · How to Reduce LLM API Costs · When to Use Small vs Frontier Models