GLM-5.3
Z.ai's newest flagship, aimed squarely at software engineering, autonomous agents and cybersecurity — and the first open-weights model to beat Claude Mythos 5 on a security benchmark.
Top open-weights coder — beats GPT-5.5 at a sixth of the cost.
Budget agentic coding at scale
You need frontier reasoning ceiling or launch-day verified benchmarks.
Compare every model's knowledge cutoff, max output, and context window.
Announced June 13, 2026; pay-per-token API live June 16. Two reasoning modes ('thinking' and 'max thinking'). GLM Coding Plan: Lite $18/mo (~$12.60 effective yearly), Pro $72, Max $160.
SWE-bench Pro 62.1 — ahead of GPT-5.5 and close to Claude Opus 4.8 on agentic coding
AA Intelligence Index 51 in max-thinking mode — well above similar-size open-weight models
MIT-licensed weights with aggressive pricing: $1.40/$4.40 first-party, cheaper via third-party hosts
Clear gap to the closed frontier: AA Index 51 vs Claude Opus 5 (61) and GPT-5.6 Sol (59)
Z.ai published no benchmark numbers at launch — buyers depended on third-party evals that arrived weeks later
What people actually use GLM-5.2 for.
Agentic coding — 62.1 SWE-bench Pro, ahead of GPT-5.5 at ~1/6th the price
Coding-plan subscriptions from ~$12.60/mo effective for individual developers
Self-hosted or third-party-hosted deployments (DeepInfra from ~$0.75/1M input)
The nearest models people weigh against it, and what actually separates them.
vs GLM-5.3 — Against GLM-5.3 (Z.ai), GLM-5.2 lands within a few percent on price. Which one wins depends on whether context depth or latency is your constraint.
vs DeepSeek V4-Pro — Against DeepSeek V4-Pro (DeepSeek), GLM-5.2 costs about 78% more per token. DeepSeek V4-Pro is the one to check first if the price difference matters more than the ceiling.
vs DeepSeek V4-Flash — Against DeepSeek V4-Flash (DeepSeek), GLM-5.2 costs about 93% more per token and answers slower. DeepSeek V4-Flash is the one to check first if the price difference matters more than the ceiling.
Price History
→0% since Aug 7
25 data points · tracked daily since Aug 7, 2026
Budget agentic coding at scale. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Similar models worth checking before you commit.
Z.ai's newest flagship, aimed squarely at software engineering, autonomous agents and cybersecurity — and the first open-weights model to beat Claude Mythos 5 on a security benchmark.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
A 284B-parameter (13B active) MoE workhorse re-post-trained for agentic and coding tasks — beats the V4-Pro preview on every published agent benchmark at ultra-commodity pricing.
GLM-5.2 costs $1.4 per million input tokens and $4.4 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $22.80 at list price, before any batch or caching discounts.
GLM-5.2 has a 1M tokens context window, with up to 128k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
GLM-5.2 is best for budget agentic coding at scale. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and balanced speed.
You need frontier reasoning ceiling or launch-day verified benchmarks.
DeepSeek V4-Pro (DeepSeek) at $0.43/1M/1M input against GLM-5.2's $1.40/1M/1M — roughly 78% less per token all in. Best open-weights flagship — near-frontier coding at a tenth of the price. Compare it first if GLM-5.2's pricing is the thing stopping you.
DeepSeek V4-Flash — fast against GLM-5.2's balanced, with 1M tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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