GLM-5.1 just topped SWE-Bench Pro at 58.4, beating GPT-5.4 (57.7) and Claude Opus 4.6 (57.3). But benchmarks don’t tell the whole story. Here’s how these three models actually compare for coding work.

The contenders

	GLM-5.1	Claude Opus 4.6	GPT-5.4
Developer	Z.ai (Zhipu AI)	Anthropic	OpenAI
Parameters	754B MoE (40B active)	Undisclosed	Undisclosed
Context	200K	200K	128K
License	MIT (open source)	Proprietary	Proprietary
SWE-Bench Pro	58.4	57.3	57.7
Training hardware	Huawei Ascend 910B	NVIDIA	NVIDIA

Coding benchmarks

On SWE-Bench Pro — the hardest coding benchmark that tests multi-file, multi-step issue resolution — GLM-5.1 leads by a narrow margin. The differences are small (about 1 point), which means in practice all three models are roughly comparable on complex engineering tasks.

Where GLM-5.1 stands out is on AIME (95.3%) and its internal coding eval where it reaches 94.6% of Claude Opus 4.6’s performance. The gap has closed dramatically from GLM-5, which scored 35.4 on the same internal eval vs GLM-5.1’s 45.3.

Agentic capabilities

This is where the models diverge significantly:

GLM-5.1 is built for marathon sessions. Z.ai optimized it specifically for “productive horizons” — how long an agent can stay on track over extended autonomous work. It can maintain goal alignment over thousands of tool calls and work on a single task for up to 8 hours. It breaks problems down, runs experiments, reads results, and self-corrects.

Claude Opus 4.6 excels at careful, thorough code analysis. It’s the best at understanding large codebases and producing clean, well-structured code. Anthropic’s new Managed Agents platform makes it easy to deploy Claude-powered agents at scale. Claude Code remains the gold standard for terminal-based AI coding.

GPT-5.4 with Codex integration is strong on autonomous coding through Codex CLI. It dominates Terminal-Bench 2.0 at 77.3% and has the fastest coding experience. OpenAI’s context compaction technology helps it handle long sessions efficiently.

Pricing

This is where GLM-5.1 has a massive advantage:

	GLM-5.1	Claude Opus 4.6	GPT-5.4
Self-hosted	Free (MIT license)	Not available	Not available
GLM Coding Plan	$3-10/month	—	—
API (per 1M tokens)	~$1-2 input, ~$2-3 output	~$15 input, ~$75 output	~$10 input, ~$30 output
Subscription	—	$20/month (Pro)	$20/month (Plus)

If you self-host GLM-5.1, your per-token cost is effectively zero after hardware. Even through Z.ai’s Coding Plan at $3/month, it’s dramatically cheaper than Claude or GPT-5 API pricing.

The catch: self-hosting a 754B model requires serious hardware. Quantized to 4-bit, you still need ~200GB+ of memory.

When to use each

Choose GLM-5.1 when:

• You need long-running autonomous coding (hours, not minutes)
• Cost is a primary concern
• You want to self-host for privacy/compliance
• You’re building custom AI coding agents
• You need MIT-licensed model weights

Choose Claude Opus 4.6 when:

• You want the best code quality and reasoning
• You’re already in the Claude Code ecosystem
• You need Anthropic’s Managed Agents platform
• You value careful, thorough analysis over speed

Choose GPT-5.4 when:

• You need the fastest coding experience
• You’re using Codex CLI or OpenAI’s ecosystem
• Terminal-based tasks are your primary workflow
• You want the broadest tool integration

The real question: does the benchmark lead matter?

Honestly? Not much. A 1-point difference on SWE-Bench Pro is within noise. What matters is:

1. GLM-5.1 is open source. You can run it, modify it, fine-tune it, and deploy it however you want. Claude and GPT-5 are black boxes.
2. The 8-hour session capability is unique. No other model claims this level of sustained autonomous coding.
3. The pricing gap is enormous. $3/month vs $20/month vs API costs that can run hundreds per day.

For most developers, the practical choice comes down to: do you want convenience (Claude/GPT-5 subscriptions) or control and cost savings (GLM-5.1)?

Related: GLM-5.1 Complete Guide · How to Use GLM-5.1 with Claude Code · Best AI Models for Coding Locally