GLM 5.2 vs GPT-5.5 for Coding: Open vs Closed (2026)

OpenAI's GPT-5.5 is the most-deployed coding model on the planet. GLM 5.2 from Zhipu Z.ai (launched June 13, 2026) is a leading open-weights challenger. The two represent the cleanest version of the open-vs-closed trade-off in coding: GPT-5.5 is the model that just works at premium price; GLM 5.2 is the model that ships its weights to your data centre at a fraction of the cost. This piece is the engineering-team version of that decision.

GLM 5.2 vs GPT-5.5: at a glance

What do the current coding benchmarks show?

GPT-5.5 is the model to beat on the public boards. Above 85% on LiveCodeBench, mid-to-high 70s on SWE-bench Verified with the standard scaffold, and 38.36 on Artificial Analysis's Intelligence Index v4.3.2 at its Xhigh reasoning tier (36.98 at High), measured October 2026. That is 4.65 points ahead of GLM-5.2's 33.71 at Max — a real lead, but a far smaller one than “the model to beat” implies, and GPT-5.5 is no longer near the top of AA's overall field, which has moved on by roughly twenty index points since April. Both figures here are v4.3.2; AA rebased from v4.1.1 during 2026 with no conversion factor, so any score you find for either model in an older post is on a different scale. What GPT-5.5 does still own is coverage: the independent benchmark community has probed it across thousands of public evals, so whatever your workload is, there is probably a published number close to it.

GLM 5.2 has no vendor-published benchmarks at launch. Its parent (GLM 5.1) was state-of-the-art on SWE-Bench Pro at 58.4 (ahead of GPT-5.4 at 57.7 then), led Terminal-Bench 2.0 at 63.5, and sustained 8-hour autonomous coding sessions. Whether 5.2 holds those gains plus extends them with the 1M window is the question that gets answered when independent benches drop, likely 1-2 weeks after the API and open weights arrive.

The honest read: GPT-5.5 is the known quantity; GLM 5.2 is the credible but unproven challenger. If your team can't tolerate a quality regression on the eval suite that already runs against GPT-5.5, the right move is to wait for the independent numbers before piloting.

How different is the context window story?

Both nominally support a 1M-token context — but with caveats.

Correcting this section: GPT-5.5's context window is 1,050,000 tokens for everyone, per OpenAI's model reference — there is no 256K standard tier and no gating. The real constraint is priced rather than capped. Any request with more than 272K input tokens is billed at 2× input and 1.5× output for the entire session, standard, batch and flex alike. So a run touching 800K input tokens costs $8 on input, not $4 — and every output token in that same session costs $45/M instead of $30/M. That is why teams cap context around 200K-270K on GPT-5.5: not because the window stops, but because crossing 272K silently reprices the whole call.

GLM 5.2's 1M context is the default across every GLM Coding Plan tier. Z.ai calls it “usable” (the model demonstrably retains comprehension across the full input, not just “accepts the bytes without erroring”). On the Coding Plan, the marginal cost of using the full window is zero up to your monthly limits.

If repo-scale agents on monorepos are a real part of your workload, GLM 5.2's 1M context is structurally cheaper at the same input size. If you're rarely hitting 200K, the gap is mostly theoretical.

What do the token economics look like?

This is the clearest gap in the comparison.

GPT-5.5 at $5 input / $30 output per million tokens is among the most expensive frontier models to run. A typical agentic coding run that produces 200K of tool calls and reasoning lands at $6-8. Multiply by daily team usage and the bill is real engineering line-item territory.

GLM-5.2's metered API landed at $1.40 input / $0.26 cached input / $4.40 output per million tokens on Z.ai — inside the range this article predicted. On sticker price that is 3.6× cheaper on input and 6.8× cheaper on output.

But per-token rates overstate the gap, and this is the number that actually matters. Artificial Analysis publishes cost-to-complete-the-benchmark alongside its scores, which prices in how many tokens each model burns to finish the same work. GLM-5.2 costs $1.47 per Index task; GPT-5.5 costs $2.63. That is a 1.8× gap, not 5-10× — GLM-5.2 spends a large part of its cheap per-token advantage on extra reasoning tokens to reach comparable answers. If you are building a business case on a 6.8× saving, re-run it on per-task cost before you present it.

For organizations spending $5K+/month on agentic coding inference, the math is hard to ignore: even a 10% quality regression on GLM 5.2 can be offset by a 5× cost reduction. For organizations spending under $500/month, the gap is real but not material — quality and reliability matter more.

Does multi-modal tip the decision?

If you need image input (design specs, mockups, screenshots, diagrams), GPT-5.5 is the only choice between these two. GLM 5.2 is text + code only; Z.ai keeps vision in a separate line (the GLM-V family). Audio is not a reason to pick GPT-5.5, which an earlier version of this page got wrong: OpenAI documents GPT-5.5's input modalities as text and image only, with the Realtime, realtime-transcription and Live endpoints all unsupported for this model. For interview transcription or voice commands you need a dedicated speech model on either side of this comparison — Z.ai's own GLM-ASR-Nano is one cheap option at $0.03/M tokens.

For pure-text agentic coding — the most common case for engineering teams — multi-modal isn't a factor.

What about self-hosting and data control?

GPT-5.5 is API-only. Code, prompts, and reasoning traces go to OpenAI; there's no on-prem option. For regulated industries (defense, healthcare with strict data residency, financial services with sovereign-data rules), the answer is “don't.”

GLM 5.2's MIT-licensed open weights have been on Hugging Face as zai-org/GLM-5.2 since 16 June 2026 (681K downloads in the last 30 days). Self-host on your own H100 cluster, run inside an air-gapped network, fine-tune on internal proprietary code. The cost is operational complexity (4-8 H100s for serviceable serving); the inference-engine lag that applied at launch is long resolved. Worth noting for anyone planning a self-host roadmap: this MIT grant is now GLM-5.2's most durable advantage over its own successors, because GLM-5.3 ships under Z.ai's bespoke glm-5.3 licence rather than MIT. Only GLM-5.3-Flash kept MIT terms.

For the broader self-hosting playbook see our self-hosting LLMs guide.

Who should pick GLM 5.2?

• Teams burning >$5K/month on GPT-5.5 agentic inference. The cost math wins so quickly that even a measurable quality regression is acceptable.
• Regulated or sovereign-data shops. MIT weights + self-hosting is the only path; OpenAI isn't an option.
• Repo-scale agents. The 1M-token window at zero marginal cost on the Coding Plan changes what your agents can do.
• Research teams wanting fine-tuning leverage. Open weights mean SFT, DPO, RLHF on internal code corpora — none of that is on the table with GPT-5.5.

Who should stay on GPT-5.5?

• Greenfield agent products targeting customers. Reliability, ecosystem maturity, and the universe of integrations matter more than the cost gap when you're shipping something new.
• Multi-modal workloads. Image, audio, mixed-input agents — GPT-5.5 is the only viable option of the two.
• Teams whose evals are tuned to GPT-5.5 quirks. Prompts, tool schemas, output parsers, fallback logic — all calibrated. The switching cost is real.
• Low-spend teams. If you're spending under $500/month on coding inference, the cost win on GLM is real but not transformative. Pay the OpenAI tax for the production-grade comfort.

The real decision tree

1. Monthly inference cost > $5,000? Pilot GLM 5.2 on a representative subset of your eval suite. Track quality delta vs cost delta.
2. Sovereign-data, regulated, or air-gapped requirements? GLM 5.2, self-hosted. Only option.
3. Image input in the loop (mockups, screenshots, diagrams)? GPT-5.5 — hard wall on GLM 5.2. Audio input? Neither: GPT-5.5 is text and image only, so you need a separate speech model regardless.
4. Greenfield agent product targeting external customers? GPT-5.5 until GLM 5.2 has independent numbers and broader ecosystem support.
5. None of the above clearly applies? Stay with what your team is most productive on, and re-check when GLM 5.2's independent benchmarks land.

Post-launch reality (June 15, 2026)

Two days after Z.ai shipped GLM 5.2 on June 13, here is what is actually confirmed vs still pending. We are pulling from the launch announcement, the Hacker News reception thread, vendor docs, and early third-party reviewers.

What is live today on the Coding Plan

• GLM 5.2 shipped included on every Coding Plan tier at launch: Lite $10/mo, Pro $30/mo, Max $80/mo. That is no longer the plan. As of October 2026 the Coding Plan is credit-metered from $18/month (Lite: 2,000 five-hour credits, 10,000 weekly), serves GLM-5.3 and GLM-5.3-Flash only, and auto-routes any GLM-5.2 or GLM-5.1 request to GLM-5.3. Usage outside 14:00–18:00 Singapore time on weekdays bills at half the credit rate.
• Drop-in tool integrations confirmed at launch: Claude Code, Cline, OpenCode, Roo Code, Goose, Crush, OpenClaw, Kilo Code — all via the OpenAI-compatible endpoint (three settings.json changes for Claude Code; nothing custom needed).
• Cursor, Continue and Aider are NOT yet wired. Cursor has an open community thread requesting GLM-5 support but no merged work; expect community config repos in the weeks after the open-weights drop.
• Two thinking-effort levels exposed: High and Max — no Low/Auto. Thinking adds roughly 30-80% to first-token latency and roughly halves throughput on long runs.

What is still pending (as of June 15)

• Standalone per-token API — live and published. Z.ai lists GLM-5.2 at $1.40 input / $0.26 cached input / $4.40 output per M tokens, identical to GLM-5.1 and to GLM-5.3. The sizing guess in the original post was exactly right.
• MIT-licensed open weights — shipped on 16 June 2026 as zai-org/GLM-5.2, three days after launch, under plain MIT.
• Hosted-provider endpoints (Together, Fireworks, DeepInfra, Groq, OpenRouter) — none list GLM 5.2 yet because the weights are not public. Expect 3-10 day catch-up after the MIT drop based on the GLM 5.1 cadence; Fireworks and DeepInfra were first on 5.1.
• chat.z.ai still serves GLM 5.1 in the free chatbot tier; 5.2 chatbot rollout is part of the same "next week" batch.

What independent benchmarks exist

Honest answer: none on the standard suites yet. As of 48 hours post-launch no third party has published SWE-bench Verified, SWE-bench Pro, LiveCodeBench, Terminal-Bench 2.0, AIDER Polyglot, GPQA Diamond, or HumanEval scores specifically for 5.2. Artificial Analysis, vals.ai, lmcouncil.ai and the SWE-bench Pro Leaderboard all showed GLM 5.1 as the most recent Zhipu entry at that point, so anyone quoting a SWE-bench number for 5.2 in mid-June was conflating it with 5.1. Updated October 5, 2026: those boards scored 5.2 long ago, and the figure we printed in August was on the retired index. On Artificial Analysis's current Intelligence Index v4.3.2, GLM-5.2 scores 33.71 at Max and 22.43 with reasoning off, at $1.47 per Index task, against GPT-5.5's 38.36 at Xhigh and $2.63 per task. The “53” was a v4.1.1-era number; AA rebased with no conversion factor, so the two are not comparable and the lower figure is not a capability regression. The non-AA figures from that pass — vals.ai SWE-bench Verified 82.80%, LiveBench 73.2, LMArena 1471 — were accurate as of August 2026; the vals.ai rank is dropped here because the field has grown since. GLM-5.3 is scored now too: a metered API went live 18 August 2026 and AA measures it at 44.78 at Max, ahead of both models on this page.

What we DO have: the GLM 5.1 baseline holds well — 58.4 on SWE-Bench Pro (state-of-the-art at that time, narrowly ahead of GPT-5.4 and Claude Opus 4.6), 63.5 on Terminal-Bench 2.0 standalone (66.5 with Claude Code scaffolding), 68.7 on CyberGym, 70.6 on τ³-Bench, 71.8 on MCP-Atlas Public Set. If 5.2 holds these gains while extending to 1M context, it is a peer-class flagship; that is the bet community devs are taking until the third-party runs land.

Community sentiment after the first 48 hours

The Hacker News reception thread (269+ points, 146 comments within hours) split into two consistent camps:

• Positive — "punches above its weight" on UI/design code, code taste, and modern conventions. One commenter described shipping a non-trivial GTK/Rust/Lua app where "GLM wrote 93%." Another flagged 1M context as the upgrade most likely to matter in practice: stop chunking files, just dump the relevant subset.
• Cautious — "about six months behind the frontier labs, similar to Opus in January" on architecture-heavy, multi-file reasoning. Run-to-run variance and harness sensitivity (Terminal-Bench swung 40.4% → 48.3% on GLM 5 depending on agent wrapper) are unresolved carry-overs from earlier GLM releases.

The HN top comment captures the practical verdict: "Test it today if you are already on the Coding Plan; do not rebuild your stack around it until third-party benchmarks land next week."

Architecture details that matter for capacity planning

Same architecture family as GLM 5/5.1: 744B total parameters / ~40B active per token, 384 experts, 61 layers with Multi-head Latent Attention, DeepSeek Sparse Attention for the long context, 28.5T pretrain tokens. For self-host capacity planning the practical numbers are:

• BF16 weights: ~1.65 TB on disk
• FP8 weights: ~800 GB on disk
• AWQ/GPTQ INT4: ~200 GB on disk
• Production sweet spot: 8× H200 SXM (1,128 GB HBM) at FP8 with room for the 1M-token KV cache. 8× H100 80GB (640 GB) is too tight for FP8 + long context — works only at ≤128K with aggressive KV offload.
• vLLM and SGLang already have GLM 5/5.1 recipes that 5.2 will load on the same code paths once the config drops. TensorRT-LLM lags by a few weeks on new architectures.

Legal and compliance notes

• The MIT license, when it ships, has no field-of-use restrictions, no MAU threshold, and no acceptable-use clause. The only obligations are the standard copyright-notice + no-warranty boilerplate.
• Zhipu has been on the US BIS Entity List since January 15, 2025. Downloading and using MIT-licensed open weights is not a regulated export under current EAR readings, BUT US federal customers and most defense primes will not approve a Chinese-origin model regardless of license — treat as effectively blocked for FedRAMP, DoD, and IC workloads.
• EU AI Act: GLM 5.2 is a GPAI model with likely systemic-risk-tier compute (10^25 FLOPs). Zhipu has not signed the GPAI Code of Practice and has not published a model card or training-data summary, which leaves the full Article 53 burden on downstream EU deployers. Finance, health and critical-infrastructure use cases need to wait for Annex XI documentation.

Bottom line vs GPT-5.5, as of October 2026: GPT-5.5 leads on capability (38.36 vs 33.71 on Intelligence Index v4.3.2) and on ecosystem depth. GLM-5.2 leads on cost — but by 1.8× on cost per Index task, not the 6-10× the per-token rates imply, because it burns more tokens to get there. Context is a wash: GPT-5.5 carries 1.05M tokens and GLM-5.2 carries 1M, though GPT-5.5 reprices the whole session above 272K input. GLM-5.2's one unmatched advantage is MIT weights on your own hardware.

If cost is genuinely the reason you are reading this, the right comparison is no longer on this page. GLM-5.3-Flash scores 41.81 on Intelligence Index v4.3.2 — above GPT-5.5's 38.36 — at $0.15/$0.50 per million tokens and $0.25 per Index task against GPT-5.5's $2.63. That is a tenth of the cost per unit of work for a higher measured score, under an MIT licence. Benchmark it on your own eval suite before you commit either way.

FAQ

Is GLM 5.2 better than GPT-5.5 for coding?

No, on measured capability. The independent runs have landed: Artificial Analysis scores GPT-5.5 at 38.36 on Intelligence Index v4.3.2 (Xhigh tier) against GLM-5.2's 33.71 (Max tier). GLM-5.2 is cheaper per unit of work ($1.47 vs $2.63 per Index task) and is the only one of the two whose weights you can download and run yourself. It is not the better coder. GLM-5.3-Flash, which postdates this comparison, does beat GPT-5.5 on the index — 41.81 — at a tenth of the cost per task.

How much cheaper is GLM 5.2 vs GPT-5.5?

Two answers, and they differ by a lot. On sticker price: GLM-5.2 is $1.40 / $4.40 per million tokens against GPT-5.5's $5 / $30, so 3.6× cheaper on input and 6.8× on output — and more than that above 272K input tokens, where GPT-5.5 doubles input and adds 50% to output for the whole session. On cost to actually finish the work, which is the honest measure: Artificial Analysis puts GLM-5.2 at $1.47 per Index task and GPT-5.5 at $2.63, a 1.8× gap. The difference between those two ratios is reasoning tokens. Budget against the per-task figure.

Can I run GLM 5.2 on my own hardware?

Yes. The MIT-licensed weights have been on Hugging Face as zai-org/GLM-5.2 since 16 June 2026. Plan for 4-8 H100s for serviceable serving at full 1M context. GPT-5.5 cannot be self-hosted under any circumstances.

Does GLM 5.2 support image or audio input?

No, GLM 5.2 is text + code only. For image-to-code, GPT-5.5 is the choice of the two — it accepts text and image input. Neither accepts audio: OpenAI documents GPT-5.5 as text-and-image in, text out, with the Realtime and transcription endpoints unsupported, so voice-driven agents need a separate speech-to-text model on either stack.

Should I switch my production agent stack today?

The independent benchmarks landed long ago, so the honest answer has changed. If you are cost-constrained, run a side-by-side pilot — but pilot GLM-5.3-Flash rather than GLM-5.2, because it scores above GPT-5.5 on Artificial Analysis's v4.3.2 index (41.81 vs 38.36) at roughly a tenth of GPT-5.5's cost per Index task, under MIT. Pilot GLM-5.2 specifically only if you need that exact checkpoint pinned on your own hardware for reproducibility or compliance. Stay on GPT-5.5 if your tool schemas and evals are tuned to it and the bill is not the binding constraint.

Related reading

• GLM 5.2 Just Launched: 1M Context, Coding-First (Day-One Brief)
• GPT-5.5 Complete Guide 2026
• AI Coding Agents Complete Guide 2026
• Self-Hosting LLMs Complete Guide 2026