An MIT-licensed model that costs $3 per million output tokens just beat a $25 proprietary frontier on most major benchmarks — coding, science, and agentic tasks included.
GLM 5.2 vs Claude Opus 4.6 is not a clean David-vs-Goliath story, though. GLM 5.2 burns nearly twice the tokens per task as some peers, doesn't support image input, and has a younger ecosystem. Opus 4.6 has been succeeded by 4.7 and 4.8 — this comparison has a shelf life.
Here's what matters for the decision.
Everything in One Table
GLM 5.2 | Claude Opus 4.6 | |
|---|---|---|
Developer | Z.ai (Zhipu AI) | Anthropic |
Released | June 16, 2026 | February 4, 2026 |
Architecture | MoE, 753B total / 40B active | Dense (undisclosed) |
License | MIT (open-weight) | Proprietary |
Context window | 1M tokens | 1M tokens |
Max output | 128K tokens | 128K (300K via batch) |
Image input | No | Yes |
Thinking modes | High, Max | Low, Medium, High, Max |
SWE-bench Pro | 62.1% | 51.9–53.4% |
Terminal-Bench | 81.0% (v2.1) | 65.4% (v2.0) |
HLE (with tools) | 54.7% | 53.0% |
GPQA Diamond | 89–91.2% | 84.0% |
BigLaw Bench | Not reported | 90.2% |
Intelligence Index | 51 (highest open-weight) | ~44 (max effort) |
Input price | $0.95/M (DeepInfra) | $5.00/M |
Output price | $3.00/M (DeepInfra) | $25.00/M |
Cheapest available | $0.77/$2.42 (OpenRouter) | $5.00/$25.00 |
Speed | ~197 tokens/sec | ~46 tokens/sec |
Self-hostable | Yes (MIT) | No |
Note on SWE-bench Pro for Opus 4.6: scores range from 47.1% (Scale private) to 53.4% (Anthropic scaffold) depending on evaluation setup. Terminal-Bench versions also differ (v2.0 vs v2.1), so direct comparison is imperfect.
Where GLM 5.2 Wins
Coding. SWE-bench Pro 62.1% vs Opus 4.6's 51.9–53.4%. Terminal-Bench 81.0% vs 65.4% (different versions, but the gap is wide). GLM 5.2 is the strongest open-weight model on every major coding benchmark.
Science. GPQA Diamond 89–91.2% vs 84.0%. The vendor-reported score (91.2%) vs independent evaluation (~89%) shows some variance, but GLM 5.2 leads on both.
Cybersecurity. Semgrep's IDOR benchmark: GLM 5.2 scored 39% F1 with a basic prompt, beating Claude Code (on Opus 4.6) at 37% F1. Cost: ~$0.17 per vulnerability found. Single benchmark, single vulnerability class — but an open-weight model outperforming a frontier agent on a reasoning-heavy security task is notable.
Speed. ~197 tokens/sec vs ~46 tokens/sec. About 4x faster.
Price. Output $3/M vs $25/M — 8.3x cheaper per token.
Where Opus 4.6 Wins
Legal reasoning. BigLaw Bench 90.2%, highest of any Claude model, 40% perfect scores. GLM 5.2 has no comparable legal benchmark reported.
Aggregate knowledge work. Despite GLM 5.2's higher GPQA Diamond score, Opus 4.6 leads on GDPval-AA (professional knowledge work) with an aggregate knowledge gap of 76.2 vs 67.2. The pattern: GLM 5.2 handles hard science questions well, but Opus 4.6 is stronger across the breadth of professional knowledge tasks.
Multimodal input. Opus 4.6 accepts images. GLM 5.2 is text-only.
Ecosystem. Claude Code, Anthropic API, Bedrock, Vertex AI — mature tooling for tool use, structured outputs, and the compaction API. GLM 5.2 has 6+ providers but fewer production-grade integrations.
Effort control. Four levels (low–max) vs two (high, max). Finer granularity for cost optimization on simple tasks.
The Token Verbosity Catch
GLM 5.2 burns ~43,000 output tokens per task in coding evaluations. MiniMax M3 uses ~24,000; Kimi K2.6 ~35,000.
Model | Tokens/task | $/M output | Cost/task |
|---|---|---|---|
GLM 5.2 | ~43K | $3.00 | ~$0.13 |
Kimi K2.6 | ~35K | $2.50 | ~$0.09 |
Opus 4.6 | ~30K (est.) | $25.00 | ~$0.75 |
Per task, GLM 5.2 is ~6x cheaper than Opus 4.6 — not the 8x that per-token pricing suggests. Still a large gap, but benchmark on your own workloads and measure total token consumption.
The Open-Weight Factor
Self-hosting. 40B active parameters (MoE) make inference more feasible than a dense 753B model. Supports vLLM, SGLang, and standard frameworks.
Data privacy. Self-hosting keeps data off third-party servers. For regulated industries, this can matter more than any benchmark.
Fine-tuning. Open weights enable domain adaptation. Opus 4.6 is a black box.
No lock-in. 6+ providers plus self-hosting. No dependency on a single vendor's pricing or policy.
For teams exploring open-source programming models, GLM 5.2 is a significant capability jump.
The Expiration Date
Opus 4.6 shipped February 2026. Opus 4.8 (Intelligence Index 56) and Sonnet 5 (matches Opus 4.6, lower price) have since arrived.
Why compare GLM 5.2 to Opus 4.6 then?
Opus 4.6 still powers many Claude Code setups and production systems
The price-performance contrast is sharpest at this tier — GLM 5.2 credibly challenges Opus 4.6 at 1/8 the cost
Against Opus 4.8, GLM 5.2 competes on coding but falls behind on breadth
Decision Tree
1. Need image input? → Opus 4.6+. GLM 5.2 is text-only.
2. Data privacy or self-hosting required? → GLM 5.2. Only option you can run on your own hardware.
3. Primary workload is coding/agentic? → GLM 5.2 — competitive or better, 6–8x cheaper.
4. Primary workload is legal or broad knowledge work? → Opus 4.6+. Aggregate knowledge gap is consistent.
5. Cost-constrained at scale? → Start with GLM 5.2, route hard cases to Opus. API aggregators make hybrid routing easy.
6. No strong constraint? → Default GLM 5.2. The 8x price gap makes it the rational starting point.
Frequently Asked Questions
Is GLM 5.2 really as good as Claude Opus 4.6?
On coding (SWE-bench Pro: 62.1% vs ~52%) and science reasoning (GPQA Diamond: ~90% vs 84%), GLM 5.2 leads. On aggregate professional knowledge work, Opus 4.6 is stronger. Intelligence Index: GLM 5.2 at 51, Opus 4.6 at ~44.
How much cheaper is GLM 5.2?
8.3x per token ($3 vs $25/M output). ~6x per task after accounting for GLM 5.2's higher token verbosity.
Can I self-host GLM 5.2?
Yes — MIT license, 40B active parameters (MoE), supports vLLM/SGLang/xLLM/ktrans. Needs significant GPU resources but far more practical than serving a dense model of comparable total size.
Does GLM 5.2 support images?
No, text-only. Opus 4.6 handles text and images.
Why not compare to Opus 4.8?
Opus 4.8 (Intelligence Index 56) is the latest, but Opus 4.6 remains widely deployed. GLM 5.2 credibly challenges the 4.6 generation; against 4.8, it competes on coding but lags on breadth.
Is GLM 5.2 the best open-source model?
For coding, yes — top open-weight on SWE-bench Pro, Terminal-Bench, and FrontierSWE. Intelligence Index 51, highest open-weight (median: 25).
How fast is GLM 5.2 vs Opus 4.6?
~4x faster. 197 tokens/sec vs 46 tokens/sec. TTFT: 1.37s vs 1.87s.
Should I switch from Opus 4.6 to GLM 5.2?
For coding/agentic tasks at scale, evaluate seriously. Measure quality and total token consumption on your tasks. For knowledge-heavy or multimodal work, stay on Opus or consider Sonnet 5 as a cheaper Anthropic option.
Where can I access GLM 5.2?
DeepInfra ($0.95/$3.00), Z.ai ($1.40/$4.40), OpenRouter ($0.77/$2.42), Fireworks, FriendliAI, Novita, Together. Self-host via HuggingFace. For cross-model pricing, see our GPT-5.6 pricing guide.