In This Article
Introduction
If you're using OpenClaw for coding — couch coding, overnight refactors, or automated testing — model choice matters. Not all LLMs are equal when it comes to fixing real bugs in real codebases. SWE-bench measures exactly that: software engineering capability by testing models on actual GitHub issues. The scores tell you which models can handle complex coding tasks and which will struggle.
For OpenClaw users, this matters because your agent might be running code generation, debugging, or refactoring while you sleep. Pick the wrong model and you wake up to broken builds. Pick the right one and you wake up to merged PRs. Here's how the 2026 leaderboard shakes out and what it means for your setup.
What Is SWE-bench?
SWE-bench is a benchmark that evaluates LLMs on real-world software engineering tasks. It pulls actual issues from open-source repositories — Django, scikit-learn, sympy — and asks models to produce patches that fix them. The metric is straightforward: did the patch pass the project's test suite? The benchmark is hard. It requires understanding codebases, reading error messages, and writing correct fixes. Models that score well here tend to perform well on real coding tasks in OpenClaw.
SWE-bench Scores
As of early 2026, the leaderboard looks like this:
| Model | Score |
|---|---|
| Claude 4.6 Opus | 80.8% |
| GPT-5.2 | 80.0% |
| Kimi K2.5 | 76.8% |
| GLM-4.7 | 73.8% |
| DeepSeek V3.2 | 73.1% |
Claude 4.6 Opus leads by a narrow margin over GPT-5.2. Kimi K2.5 is the top open-source performer — impressive for a model you can run locally or via cheaper APIs. DeepSeek V3.2 punches above its weight: 73.1% at a fraction of the cost. For teams doing heavy coding automation, that gap between 80% and 73% can mean the difference between "it just works" and "it needs a few retries."
OpenClaw Implications
OpenClaw is model-agnostic. You can connect any of these. The question is which to use for which workload. For couch coding and complex refactors — the kind of work where a wrong fix could break production — Claude or GPT-5 is the safe choice. For cost-sensitive, high-volume tasks — Heartbeats that summarize logs, draft emails, or run simple scripts — DeepSeek or Kimi delivers good enough quality at a fraction of the cost. For pure coding with no Life OS features, Claude Code is optimized for that; OpenClaw is for when you want coding plus everything else.
Cost vs Performance
Claude and GPT-5: best performance, highest cost. A single complex coding session can run $2–5 in API calls. Kimi: near-Claude performance at lower cost — a strong middle ground. DeepSeek: 73% for roughly 1/10th the cost of Claude. If you're running dozens of coding tasks per day, that adds up. The strategy: use premium models for high-stakes coding, budget models for everything else. Many users route coding tasks to Claude and Heartbeat/summarization to DeepSeek. See pricing for the full breakdown.
When to Use Which Model
Claude 4.6 Opus or GPT-5.2: Overnight refactors, complex bug fixes, multi-file changes. When the cost of a wrong fix exceeds the cost of the API call. Kimi K2.5: Good balance of cost and quality. Solid for most coding tasks if you're watching the bill. DeepSeek V3.2: High-volume, lower-stakes work. Drafts, summaries, simple scripts. When 73% is good enough and 1/10th cost matters.
Wrapping Up
SWE-bench is one signal — not the only one. But for OpenClaw users doing real coding work, it's the most relevant. Match your model to your workload. See AI models and pricing for more.