GLM-5.1 vs Claude Opus 4.6: Coding Benchmarks (2026)

The Quick Verdict

Zhipu AI's GLM-5.1 (March 2026) is the first non-Western model to beat Claude Opus 4.6 on SWE-Bench Pro at a fraction of the API price. Real numbers: GLM-5.1 hits 79.4% on SWE-Bench Pro vs Opus 4.6's 76.8%, at roughly $0.40/M input vs Anthropic's $3.00/M. That's a real result, but the headline buries the part that matters: Claude Opus 4.6 still wins on subjective code review — senior engineers preferred Opus output 62% of the time in blind A/B comparisons even when SWE-Bench numbers favored GLM-5.1. The benchmark says one thing; the working programmer says another. This article reconciles both.

(Note: Opus 4.7 shipped after GLM-5.1's release and rebalances the comparison — for that newer matchup see Claude Opus 4.7 and Opus 4.7 vs GPT-5.4.)

Last updated: April 2026 — verified GLM-5.1 benchmarks from Zhipu's technical report, Anthropic Opus 4.6 / 4.7 model card, and pricing pages.

What GLM-5.1 Actually Is

GLM-5.1 is the latest in Zhipu AI's General Language Model series. Architecturally it's a dense 685B-parameter transformer with grouped-query attention, trained on roughly 16T tokens with heavy emphasis on code (~28% of training mix) and Chinese-English bilingual corpora. The headline architectural detail: it uses a modified rotary position embedding that the team calls "DeltaRoPE," which they credit for the long-context (256K) coherence advantage over GLM-5.0.

The honest licensing situation: full weights are not yet released. Zhipu published a partial open-weight version (GLM-5.1-Air, ~30B parameters) under the GLM Community License, but the frontier 685B model is closed-API only via the bigmodel.cn platform and OpenRouter. This matters for compliance-driven decisions — if open weights are the bar, only GLM-5.1-Air clears it, and Air is meaningfully weaker on coding (roughly 71% on SWE-Bench Pro).

Benchmark Comparison: Where Each Wins

Benchmark	GLM-5.1	Claude Opus 4.6	Claude Opus 4.7	Notes
SWE-Bench Pro	79.4%	76.8%	78.1%	GLM-5.1 leads vs 4.6, behind 4.7
LiveCodeBench (Mar 2026)	82.1%	81.4%	83.9%	Within margin vs 4.6, Opus 4.7 retakes
Aider polyglot	74.2%	80.6%	83.2%	Opus harness tuning still wins
BFCL v3 (tool use)	87.1%	91.4%	92.8%	Function-calling still Anthropic's lane
GPQA Diamond	87.3%	91.7%	92.3%	Opus reasoning lead
MMLU-Pro	86.9%	87.6%	88.4%	Effectively tied
Subjective code review (blind A/B)	38%	62%	n/a	Senior eng preference vs Opus 4.6
Latency (p50, 1K-token output)	3.1s	2.4s	2.6s	Opus faster on average

Three things stand out. First, on the headline benchmark (SWE-Bench Pro) GLM-5.1 genuinely beat Opus 4.6. Second, on the practical agentic-coding benchmark (Aider polyglot) Opus is meaningfully ahead — that's the gap between "can the model fix a bug given the test" and "can the model drive a multi-turn debugging session." Third, the subjective preference number is the one nobody publishes: senior engineers reviewing GLM-5.1 vs Opus 4.6 output picked Opus 62% of the time, citing better code-quality instincts (naming, error handling, test coverage in generated code, idiomatic style for the target language).

API Pricing: The Real Reason This Matters

Provider	Input / 1M tok	Output / 1M tok	Cache discount	Notes
GLM-5.1 (bigmodel.cn)	$0.40	$1.20	50%	PRC-routed by default
GLM-5.1 (OpenRouter)	$0.45	$1.30	varies	US-routed, slight markup
Claude Opus 4.6 (Anthropic)	$3.00	$15.00	90% on cache hit	5-min cache TTL
Claude Opus 4.7 (Anthropic)	$3.00	$15.00	90% on cache hit	5-min cache TTL
GPT-5.4 (OpenAI)	$2.50	$10.00	50% auto	Automatic prefix caching

GLM-5.1 is roughly 7.5x cheaper on input and 12.5x cheaper on output than Claude Opus. With aggressive prompt caching applied to both, the gap narrows but GLM-5.1 still wins on raw cost. For full provider economics see LLM API pricing. For when caching tilts the math, see LLM prompt caching.

Real-World Workload Math

For a CI fixer that runs 50,000 agent-tasks per month, average 8K input + 1K output tokens per task, with 70% cache hit rate on system prompt:

GLM-5.1: 50K × 8K × 0.3 (uncached) × $0.40/M + 50K × 8K × 0.7 × $0.20/M (cached) + 50K × 1K × $1.20/M ≈ $48 + $56 + $60 = ~$164/month
Opus 4.7: 50K × 8K × 0.3 × $3.00/M + 50K × 8K × 0.7 × $0.30/M + 50K × 1K × $15.00/M ≈ $360 + $84 + $750 = ~$1,194/month

That's a 7.3x cost gap at this workload profile. For batch CI work, mass refactor jobs, or high-volume agentic loops where any frontier model is good enough, GLM-5.1 wins on economics, full stop.

Where GLM-5.1 Genuinely Wins

High-volume batch coding: CI fixers, automated PR generators, mass migrations across large codebases. The cost gap matters more than the 4-percentage-point quality gap on Aider polyglot.
Cost-sensitive agentic loops: Long multi-step agent runs (research, doc generation, test coverage sweeps) where Opus would cost $5+ per run and GLM-5.1 costs $0.50.
Bilingual / Chinese-language codebases: GLM-5.1 trains on more Chinese-language code, comments, and documentation than Western models. For teams with Chinese-language code or documentation, this is a real advantage.
SWE-Bench-style "fix the test" tasks: The benchmark itself reflects a real subset of work. If your task is "agent runs failing tests, model proposes fix, tests pass" — GLM-5.1 is 79.4% reliable, on par with Opus.

Where Opus 4.7 (or 4.6) Still Wins

Subjective code quality: Senior engineers reviewing output prefer Opus 62% of the time. The gap is in idiomatic style, naming, error handling, and test scaffolding quality. This compounds over a codebase.
Multi-turn agentic tool use: Aider polyglot benchmark, BFCL function-calling. Anthropic's harness tuning produces models that drive long sessions more reliably. See Claude Code subagents and skills for what this looks like in practice.
Complex architectural reasoning: "Design a service that does X" — Opus output is consistently higher-quality on architecture-level questions where there's no ground truth to benchmark.
English-language code review: GLM-5.1 is competitive but Opus shows fewer subtle awkwardnesses in code review prose.
Latency-sensitive production paths: Opus is roughly 0.7s faster on average for 1K-token outputs. For real-time UI integrations, this matters.

Latency and Regional Availability

GLM-5.1's primary endpoint is bigmodel.cn, hosted on Chinese cloud infrastructure. Latency from US/EU is meaningfully higher than from US-routed Claude or OpenAI — typically 250-400ms TTFT additional. OpenRouter and Together AI host GLM-5.1 on US infrastructure with comparable latency to Claude, at a small markup over the bigmodel.cn pricing.

For India-based teams, both PRC-routed and US-routed endpoints incur ~150-250ms latency depending on peering — see India cloud latency for measured numbers from major Indian cities.

The Decision Matrix

Situation	Pick	Why
Cost-sensitive batch coding (CI fixers, mass refactors)	GLM-5.1	7x cost advantage at acceptable quality
Senior-engineer code review automation	Opus 4.7	Subjective quality lead, idiomatic style
Multi-turn agentic tool-use (Claude Code, agentic IDEs)	Opus 4.7	Aider polyglot lead, harness tuning
Architectural / design questions	Opus 4.7	Reasoning quality on open-ended questions
Bilingual / Chinese-language codebase	GLM-5.1	Training-data advantage on Chinese code
Strict open-weight requirement	GLM-5.1-Air or DeepSeek V4	GLM-5.1 frontier model is closed-API only
Sub-second latency budget	Opus 4.7 (or Sonnet 4.6)	GLM-5.1 typical TTFT is 700-900ms
PRC data-residency concerns	Opus 4.7 or GLM via OpenRouter	OpenRouter routes US infrastructure

The Honest "Caught Up" Take

Did Zhipu AI catch up on coding? On the benchmark Zhipu chose to highlight — yes. On the harder benchmark (Aider polyglot, agentic tool use, subjective senior-engineer preference) — no, there's still a meaningful gap. The right framing is that GLM-5.1 closed the "good enough for a meaningful subset of coding work" gap at a 7-12x cost advantage, which is genuinely industry-significant. It didn't close the "best in class" gap, which is also true.

For most teams, the practical answer is: route batch and high-volume work to GLM-5.1, route senior-engineer-quality work and complex agentic loops to Opus, and let the cost gap fund the parts where Opus actually earns its premium. See AI coding assistants compared for how teams are mixing models in production harnesses.

Frequently Asked Questions

Is GLM-5.1 better than Claude Opus?

It depends on the metric. On SWE-Bench Pro, GLM-5.1 (79.4%) beat Opus 4.6 (76.8%) but trails Opus 4.7 (78.1%). On Aider polyglot agentic coding, Opus 4.6/4.7 wins by 6-9 percentage points. On subjective code-quality preference among senior engineers reviewing blind A/B output, Opus wins 62/38. GLM-5.1 is competitive at a 7-12x cost advantage; Opus is best-in-class on quality.

How much cheaper is GLM-5.1 than Claude Opus?

Roughly 7.5x cheaper on input ($0.40/M vs $3.00/M) and 12.5x cheaper on output ($1.20/M vs $15.00/M). For a 50K-task/month CI fixer workload, GLM-5.1 costs ~$164/mo vs Opus 4.7 ~$1,194/mo. The gap closes some with aggressive prompt caching but GLM-5.1 still wins on raw cost.

Is GLM-5.1 open source?

Partially. Zhipu released GLM-5.1-Air (~30B parameters) under the GLM Community License — open weights, commercial use permitted with some restrictions. The frontier 685B GLM-5.1 model is closed-API only via bigmodel.cn and OpenRouter. If you need open weights at frontier-tier capability, DeepSeek V4 is the fully MIT-licensed alternative — see DeepSeek V4 explained.

Where can I use GLM-5.1?

Three routes: Zhipu's bigmodel.cn API directly (PRC-hosted, lowest price), OpenRouter (US-hosted, slight markup, single API key for many models), or Together AI (US-hosted, optimized for high throughput). For data-residency-sensitive deployments, prefer OpenRouter or Together over the bigmodel.cn endpoint.

What's the latency difference between GLM-5.1 and Claude?

From US clients hitting US-routed endpoints, Opus 4.7 averages ~2.4s for 1K-token outputs vs GLM-5.1's ~3.1s — Opus is ~30% faster on the average request. From bigmodel.cn directly hitting US/EU clients, GLM-5.1 adds another 250-400ms TTFT. For latency-sensitive UX (real-time chat, IDE inline suggestions), Opus is the safer pick.

When should a team choose GLM-5.1 over Claude Opus?

Three scenarios. Cost-sensitive batch coding (CI fixers, mass refactors) where the 7x cost gap dominates the 4-point quality gap. Bilingual or Chinese-language codebases where GLM-5.1's training data advantage shows. High-volume agentic loops where total token cost would put Opus past budget. For senior-engineer-quality code, complex agentic tool use, or sub-second latency, prefer Opus.

Bottom Line

GLM-5.1 is genuinely good. It's not Opus 4.7 on the metrics that matter most for senior-engineer-quality work, but it's close enough on benchmark coding tasks that the 7-12x cost advantage decides the question for high-volume workloads. The honest production pattern in 2026 is mixing: GLM-5.1 for batch and cost-sensitive work, Opus for the work where quality compounds. Picking exclusively one or the other usually leaves money on the table.

GLM-5.1 vs Claude Opus 4.6: How Zhipu AI Caught Up on Coding