GLM-5.2 vs GPT-5.5 vs Claude Opus 4.8: Full Comparison (2026)

TL;DR

• GLM-5.2 (Z.ai) costs $1.40/$4.40 per million tokens — roughly one-sixth the price of GPT-5.5 ($5/$30) and Claude Opus 4.8 ($5/$25)
• On coding benchmarks GLM-5.2 trades blows with both: it beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6) but trails Opus 4.8 (69.2)
• Opus 4.8 leads on raw intelligence (61.4 AA index vs GLM-5.2's 51), GPT-5.5 sits in the middle (60)
• GLM-5.2 is the only one of the three with open MIT-licensed weights you can self-host — but it burns ~43k output tokens per task versus ~24k for competitors

A Chinese open-weight model just landed within a few points of the best closed models from OpenAI and Anthropic — at a fraction of the cost. GLM-5.2 from Z.ai (formerly Zhipu AI) forces a real question: when an open model this cheap gets this close, does paying 6× for a closed frontier model still make sense? Here's the head-to-head on benchmarks, price, and where each one actually wins.

Which model is cheapest: GLM-5.2, GPT-5.5, or Opus 4.8?

GLM-5.2 is dramatically cheaper, at $1.40 input / $4.40 output per million tokens — about one-sixth the cost of GPT-5.5 ($5/$30) and Claude Opus 4.8 ($5/$25).

The price gap is the headline of this comparison. GLM-5.2's open-weight, MIT-licensed model undercuts both American frontier labs by a wide margin, and adds cached input at just $0.26 per million tokens.

The output-cost gap is the real story. GPT-5.5's output tokens cost 6.8× more than GLM-5.2's, and Opus 4.8's cost 5.7× more. For output-heavy workloads — code generation, long-form synthesis, agent loops — that multiplier compounds fast.

One caveat that narrows the gap: GLM-5.2 is token-inefficient. Third-party testing found it burns roughly 43,000 output tokens per task versus ~24,000 for competitors. At 1.8× the token consumption, GLM-5.2's effective cost advantage on a per-task basis is closer to 3-4× than the headline 6×. Still a large gap — just not as large as the sticker price suggests.

How do GLM-5.2, GPT-5.5, and Opus 4.8 compare on benchmarks?

Claude Opus 4.8 leads on raw intelligence and coding accuracy, GPT-5.5 is close behind, and GLM-5.2 trails by a few points overall while beating GPT-5.5 on some coding tasks.

Here's the head-to-head on confirmed third-party benchmarks:

*Sources: Artificial Analysis, third-party evaluations, June 2026. Z.ai published no official benchmarks at launch.*

Reading the numbers:

• Opus 4.8 wins on accuracy — top scores on SWE-bench Pro, FrontierSWE, Terminal-Bench, and the AA Intelligence Index. If correctness on hard tasks is your priority, it leads.
• GLM-5.2 punches above its price — it beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6), FrontierSWE, MCP-Atlas, and GDPval. For an open model at one-sixth the cost, landing ahead of GPT-5.5 on several coding evals is the surprise of mid-2026.
• GPT-5.5 sits in the middle — strong on Terminal-Bench (82.7) and the AA index (60), but no longer the clear coding leader it was at launch.

The community consensus: GLM-5.2 is roughly six months behind the frontier labs on architecture-heavy reasoning, but the gap on practical coding tasks has nearly closed.

Which model is best for coding agents?

For pure accuracy on hard coding tasks, Claude Opus 4.8 leads; for cost-efficient high-volume coding, GLM-5.2 offers the best value; GPT-5.5 is the balanced middle option with the most mature ecosystem.

Coding is where this comparison matters most, since all three are positioned as agentic coding models.

• Claude Opus 4.8 — Best for complex, high-stakes refactors and architecture work where a wrong answer is expensive. Its SWE-bench Pro lead (69.2) and dynamic workflow support for hundreds of parallel subagents make it the accuracy choice. The cost is justified when correctness matters more than token budget.

• GLM-5.2 — Best for high-volume coding where cost dominates. At $4.40 output per million tokens and beating GPT-5.5 on SWE-bench Pro, it's the value pick for agent loops, batch code generation, and teams running thousands of coding tasks daily. The token inefficiency matters here, so measure real per-task cost.

• GPT-5.5 — Best for teams already in the OpenAI ecosystem who want proven tooling, multimodal support, and the most mature agent infrastructure. It leads on Terminal-Bench (82.7) for unattended terminal workflows.

The deciding factor is usually volume. A team running occasional complex tasks should pay for Opus 4.8's accuracy. A team running coding agents at scale — where output tokens pile up fast — will find GLM-5.2's economics hard to beat, even accounting for its token inefficiency.

Should you use an open model like GLM-5.2 over closed models?

GLM-5.2's open MIT-licensed weights offer self-hosting, data control, and no vendor lock-in — advantages that matter for regulated industries and high-volume users, but come with infrastructure and maintenance costs closed APIs avoid.

This is the strategic question GLM-5.2 forces, beyond the benchmark numbers.

Arguments for GLM-5.2 (open):

• Self-hosting — run it on your own infrastructure for data residency, compliance, or air-gapped environments
• No vendor lock-in — the weights are yours under MIT license; no API can be deprecated out from under you
• Cost at scale — for very high volume, self-hosting can undercut even GLM-5.2's cheap API
• Transparency — you can inspect, fine-tune, and modify the model

Arguments for GPT-5.5 / Opus 4.8 (closed):

• Higher ceiling on hard tasks — Opus 4.8's accuracy lead is real on complex reasoning
• No infrastructure burden — no GPU clusters, no model ops, no scaling headaches
• Mature ecosystem — better tooling, multimodal support, established integrations
• Managed reliability — the provider handles uptime, not you

The practical answer for most teams: Use GLM-5.2 through an API for cost-sensitive, high-volume coding, and reserve Opus 4.8 for the hardest tasks where accuracy justifies the price. You don't have to choose one — routing between them by task type captures the best of both. A unified gateway like Velokey lets you call GLM-5.2, GPT-5.5, and Opus 4.8 through one OpenAI-compatible endpoint and switch per request.

Which model should you choose?

Choose Opus 4.8 for maximum accuracy, GLM-5.2 for maximum value, and GPT-5.5 for ecosystem maturity — or route between them by task to optimize both cost and quality.

Here's the decision in one table:

Most production teams end up using more than one. The cost difference is large enough that routing high-volume work to GLM-5.2 while keeping critical tasks on Opus 4.8 can cut your model spend substantially without sacrificing quality where it counts.

Frequently Asked Questions

Is GLM-5.2 better than GPT-5.5?

On several coding benchmarks, yes — GLM-5.2 beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6), FrontierSWE, and MCP-Atlas. On overall intelligence, GPT-5.5 leads (AA index 60 vs 51). GLM-5.2 wins decisively on price, at roughly one-sixth the cost. For cost-sensitive coding, GLM-5.2 is the better value; for general intelligence, GPT-5.5 leads.

Is GLM-5.2 as good as Claude Opus 4.8?

Not quite on accuracy. Opus 4.8 leads on SWE-bench Pro (69.2 vs 62.1), Terminal-Bench (85.0 vs 81.0), and the AA Intelligence Index (61.4 vs 51). But GLM-5.2 costs about one-sixth as much and is open-weight. For the hardest tasks, Opus 4.8 wins; for high-volume work where cost matters, GLM-5.2 is compelling.

How much cheaper is GLM-5.2?

GLM-5.2 costs $1.40 input / $4.40 output per million tokens, versus $5/$30 for GPT-5.5 and $5/$25 for Opus 4.8 — roughly one-sixth the price. However, GLM-5.2 uses ~1.8× more output tokens per task, so the effective per-task savings is closer to 3-4× than 6×.

Can I self-host GLM-5.2?

Yes. GLM-5.2 ships with open weights under an MIT license, available on Hugging Face under zai-org/GLM-5.2. It's a 744-billion-parameter Mixture-of-Experts model with ~40B active parameters per token, so self-hosting requires substantial GPU infrastructure, but there's no licensing restriction. GPT-5.5 and Opus 4.8 are closed and cannot be self-hosted.

What's the context window for each model?

All three offer large context windows. GLM-5.2 has a 1M token window by default, GPT-5.5 offers 1M tokens, and Claude Opus 4.8 offers 1M tokens. GLM-5.2's max output is 131,072 tokens per response.

Which model is best for high-volume coding agents?

GLM-5.2 offers the best economics for high-volume coding, beating GPT-5.5 on SWE-bench Pro at one-sixth the price. For tasks where accuracy is critical, Opus 4.8 leads. Many teams route by task type — GLM-5.2 for volume, Opus 4.8 for hard cases — through a unified API to balance cost and quality.

Want to compare all three on your own workload? Get your Velokey API key and call GLM-5.2, GPT-5.5, and Claude Opus 4.8 through one OpenAI-compatible endpoint — route by task to optimize cost and quality without managing three integrations.

*Last updated: June 25, 2026. Benchmark scores are from third-party evaluations (Artificial Analysis and others); Z.ai published no official benchmarks for GLM-5.2 at launch. Pricing reflects published API rates as of publication. GLM-5.2 released June 13, 2026; GPT-5.5 April 23, 2026; Claude Opus 4.8 May 28, 2026.*