GLM-5.2 vs GPT-5.5 (2026): Coding, Price, and the Open-Weight Question
Last updated: 26 June 2026
GLM-5.2 is brand-new — Z.ai shipped it to Coding Plan subscribers on 13 June 2026 and published the open weights on 16 June. Z.ai notably did not release a full official benchmark suite, so several head-to-head coding numbers below are vendor-reported and not yet independently reproduced. They are flagged as such.
The “GLM-5.2 vs GPT-5.5” matchup is the clearest example yet of open weights closing the gap on the frontier. Zhipu AI’s (now Z.ai) GLM-5.2 is a 753B-parameter Mixture-of-Experts model with ~40B active parameters, an MIT license, and a 1M-token context window — and on the independent Artificial Analysis Intelligence Index v4.1 it scored 51, the top open-weights model, while costing roughly one-sixth of GPT-5.5 per token. OpenAI’s GPT-5.5 still wins on ecosystem, multimodality, and a proven track record. Rather than bet everything on one, many teams route between them through a gateway like OrcaRouter, which serves GLM-5.2 alongside GPT-5.5 behind one endpoint.
Quick take: Pick GLM-5.2 for high-volume coding and agentic pipelines where cost-per-task dominates and you want open weights you can self-host. Pick GPT-5.5 for multimodal work, the deepest tool/app ecosystem, and battle-tested reliability. The two are close enough on coding that the deciding factor is usually price and deployment — which is exactly what makes routing between them worthwhile.
GLM-5.2 vs GPT-5.5: at a glance
| GLM-5.2 (Z.ai) | GPT-5.5 (OpenAI) | |
| Released | 13 June 2026 | 23 April 2026 |
| Architecture | 753B MoE, ~40B active | Not disclosed |
| License | MIT, open weights | Proprietary (API/app only) |
| Context window | 1M tokens | ~1.1M tokens |
| Max output | ~131K tokens | 128K tokens |
| SWE-bench Pro | 62.1 (vendor) | 58.6 |
| Terminal-Bench | 81.0 (v2.1, vendor) | 82.7 (v2.0) |
| Multimodal | ❌ text-only | ✅ multimodal input |
| API price /1M | ~$1.40 in / $4.40 out | $5 in / $30 out |
GLM-5.2 matches GPT-5.5 on the basics — and undercuts it sharply on price. GLM coding scores are vendor-reported. Sources: Z.ai, Eden AI, llm-stats, Simon Willison.
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1. Coding → GLM-5.2 leads on value (with caveats)
This is the headline. On Z.ai’s own numbers, GLM-5.2 scores 62.1 on SWE-bench Pro versus 58.6 for GPT-5.5 and edges ahead on long-horizon coding benchmarks like FrontierSWE (74.4 vs 72.6). Independently, GLM-5.2 ranks #2 on the Code Arena WebDev leaderboard, behind only a Claude model — strong corroboration that the coding strength is real.
The honest caveat: these SWE-bench figures are vendor-reported, and Z.ai didn’t publish a full official benchmark suite. GPT-5.5, meanwhile, holds a state-of-the-art 82.7% on Terminal-Bench 2.0, so terminal-centric agentic work still favors OpenAI. Treat the two as roughly co-equal on coding quality — and let cost break the tie.
2. Context window → Tie
Both ship million-token context. GLM-5.2 offers 1M tokens (a 5× jump from GLM-5.1’s 200K), and GPT-5.5 is alongside at ~1.1M. Either handles entire repositories or long document sets in one pass. One nuance: GLM-5.2 is token-hungry, burning ~43K output tokens per Intelligence Index task versus 26K for GLM-5.1 — so its low per-token price is partly offset by higher token usage on hard tasks.
3. Price → GLM-5.2, decisively
This is GLM-5.2’s biggest edge. Via third-party providers it runs about $1.40 input / $4.40 output per 1M tokens — against GPT-5.5’s $5 / $30. That’s roughly 6× cheaper on output, the dimension that dominates agentic cost. Z.ai also sells a flat GLM Coding Plan from around $18/month. For high-volume pipelines, the savings compound fast.
Comparable coding, very different price. GLM coding scores are vendor-reported. Sources: Z.ai / Eden AI, llm-stats, Simon Willison.
4. Availability and deployment → different philosophies
GPT-5.5 is API- and app-only: it rolled out to ChatGPT Plus, Pro, Business, and Enterprise users plus Codex, with the deepest plugin, tool, and integration ecosystem in the market. GLM-5.2 takes the opposite path — MIT-licensed open weights on Hugging Face that you can self-host, fine-tune, and run inside your own perimeter, with no vendor lock-in. The tradeoff is real: the open weights are a 1.5TB model that’s brutal to run locally, so most teams access it through hosted providers anyway.
5. Ecosystem and trust → GPT-5.5
GPT-5.5 has the maturity advantage: more integrations, longer production track record, and multimodal input GLM-5.2 lacks. GLM-5.2 is text-only and only days old, with independent benchmarks still catching up. For mission-critical workloads, that gap matters today — even if it’s closing fast.
The bottom line
In GLM-5.2 vs GPT-5.5, there’s no blowout. GLM-5.2 has reached the point where an open-weights model trades blows with a frontier proprietary one on coding — at roughly one-sixth the price and with the freedom to self-host. GPT-5.5 answers with multimodality, ecosystem depth, and a proven track record. The smart play isn’t loyalty to either vendor — it’s to route by task: send high-volume coding and agentic jobs to GLM-5.2 to slash cost, keep multimodal and ecosystem-dependent work on GPT-5.5, and let an LLM router handle failover and price arbitrage automatically. As independent GLM-5.2 benchmarks land, expect that routing balance to tilt further toward open weights.
Frequently asked questions
Is GLM-5.2 better than GPT-5.5 for coding? On vendor-reported benchmarks, GLM-5.2 edges ahead (62.1 vs 58.6 on SWE-bench Pro), and it ranks #2 on Code Arena WebDev independently. They’re close enough that price usually decides — and GLM-5.2 is far cheaper.
Which is cheaper? GLM-5.2, by a wide margin: ~$1.40/$4.40 per 1M tokens versus GPT-5.5’s $5/$30 — roughly 6× cheaper on output.
Is GLM-5.2 open source? Yes — it’s released under an MIT license with open weights on Hugging Face, so you can self-host and fine-tune it. GPT-5.5 is proprietary.
Are GLM-5.2’s benchmark numbers verified? Partly. Z.ai didn’t publish a full official benchmark suite, so the SWE-bench figures are vendor-reported. Its independent Artificial Analysis Intelligence Index v4.1 score of 51 (top open-weights model) is third-party verified.
Can GLM-5.2 process images? No — GLM-5.2 is text-only. GPT-5.5 supports multimodal input, which is a clear advantage for vision tasks.
Do I have to choose one? No. A gateway lets you route per task — cheap, open GLM-5.2 for bulk coding; GPT-5.5 for multimodal and ecosystem-dependent work — with automatic failover between them.
