GLM-5.2 vs GPT-5.5: The MIT Open-Weight Model That Beats OpenAI's Flagship on Pro

A $4.40/1M MIT-licensed open-weight model just beat OpenAI's $30/1M flagship on the hardest coding benchmark. GLM-5.2 — Z.ai's new 753B-parameter model released June 13 — scores 62.1% on SWE-bench Pro vs GPT-5.5's 58.6%. That's a 3.5-point lead at roughly 1/7 the cost. It also leads HLE with tools (+2.5), FrontierSWE (+1.8), MCP Atlas (+1.7), and PostTrainBench (+9.3). GPT-5.5 fights back with DeepSWE (+23.8), Terminal-Bench 2.1 (+3.0), and a massive ecosystem advantage (Codex CLI, 4M weekly devs, Amazon Bedrock). Here's the complete comparison backed by Z.ai's cross-model benchmark table published via VentureBeat. Try both on CodingFleet.

Try both models on CodingFleet

Full Benchmark Comparison

Sources: All GLM-5.2 scores from Z.AI cross-model comparison table published via VentureBeat | GPT-5.5 scores from Vellum, Vals.ai, OpenAI announcements. Pricing from Z.AI API and OpenAI API. All scores vendor-reported from Z.AI's published table.

SWE-bench Pro: The 3.5-Point Upset

The defining number. GLM-5.2 at 62.1% vs GPT-5.5 at 58.6%. A 3.5-point gap on the hardest real-world coding benchmark — at 1/7 the cost. VentureBeat's analysis captures the significance: "Z.ai's open-weights GLM-5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for 1/6th the cost." The MIT license means enterprises can download the weights from HuggingFace, fine-tune on proprietary codebases, and deploy air-gapped — with no vendor lock-in, no API dependency, and no usage-based billing surprises.

DeepSWE: GPT-5.5's 23.8-Point Fortress

The widest gap on any shared benchmark — and it favors GPT-5.5 decisively. DeepSWE at 70.0% vs GLM-5.2 at 46.2%. This benchmark tests the hardest software engineering tasks: deep multi-file refactors, complex dependency resolution, architectural changes across large codebases. GPT-5.5's Codex CLI infrastructure — with cloud sandbox execution, 24+ hour unattended runs, and kernel-level sandboxing — was built specifically for these workloads. GLM-5.2's 1M context window and long-horizon capabilities are architecturally relevant here, but the benchmark gap suggests the Codex harness provides a structural advantage that raw model capability alone can't close.

FrontierSWE: GLM's Near-Opus Territory

GLM-5.2 at 74.4% on FrontierSWE — just 0.7 points behind Claude Opus 4.8 (75.1%). GPT-5.5 at 72.6%. This benchmark tests long-horizon software engineering across frontier-level tasks, and DataCamp reports Z.ai claims GLM-5.2 "ranks #1 open-source and #3 overall on FrontierSWE, nearing Claude Opus 4.8." For an MIT-licensed model at $4.40/1M output, approaching Opus 4.8 ($25/1M) on any benchmark is remarkable.

Architecture & Ecosystem

Why GLM-5.2 Wins: The Open-Weight Disruption

GLM-5.2 is Z.ai's third MIT-licensed flagship in six months — GLM-5 (February), GLM-5.1 (April), GLM-5.2 (June). The entire lineage was trained on Huawei Ascend chips, proving frontier-scale models don't require NVIDIA hardware. The 1M context window (5× GLM-5.1's 200K) handles entire codebases in a single session. Setup is trivial: set ANTHROPIC_DEFAULT_SONNET_MODEL=glm-5.2[1m] in Claude Code and you're running. Two thinking modes (High for speed, Max for depth). The VentureBeat analysis highlights the geopolitical dimension: "An MIT license guarantees no regional limits and allows technical access without borders — increasingly appealing following the Trump Administration's export control directive." AICodeKing (129K YouTube subscribers) tested GLM-5.2 and reported "scores around 81.43 on my benchmark, only about 6% below Opus 4.8 and Fable."

Why GPT-5.5 Wins: The Ecosystem Moat

GPT-5.5's Codex ecosystem is the most mature agentic coding platform in existence. 4 million weekly active developers. Codex CLI: install with npm, run from any project root, structured agent loops with /plan /exec /review. Cloud sandbox: 24+ hour unattended runs, kernel-level isolation. 6 IDE extensions: VS Code, JetBrains, Visual Studio, Neovim, Xcode, Eclipse. Amazon Bedrock integration for enterprise AWS deployments. Omnimodal input: text, image, audio, video — GLM-5.2 is text-only. The DeepSWE 23.8-point gap isn't about model intelligence — it's about infrastructure. For teams building production coding agents where harness maturity, enterprise compliance, and multimodal input matter more than raw Pro scores, GPT-5.5 is the safer bet. Nipralo's review notes: "For agentic coding tasks like multi-file refactors and long debug sessions, GPT-5.5 wins consistently."

Pricing: 6.8× Economics

At 100M output tokens/month:

• GLM-5.2 (API): $440 output + $140 input = $580/month
• GPT-5.5 (API): $3,000 output + $500 input = $3,500/month
• GLM-5.2 (Coding Plan Pro): ~$15-19/month flat — unlimited prompts per 5-hour cycle

The $2,920 monthly difference funds an entire additional development team's AI tooling — or a self-hosted GLM-5.2 deployment on your own GPUs with zero API costs. Lushbinary's pricing guide notes GLM Coding Plan tiers start at $3/month (Lite) and scale to $80/month (Max) for heavy agentic work.

Which Model Should You Use?

Conclusion: The Open-Weight Model Beats the Proprietary Flagship on Pro

GLM-5.2 leads GPT-5.5 on 7 of 12 shared benchmarks — including SWE-bench Pro, the one that matters most for real-world coding. At 1/7 the cost, with an MIT license and Anthropic API compatibility, it represents the direction the AI coding market is heading: open-weight models that match or beat proprietary flagships on core benchmarks while offering deployment freedom that closed models can't match.

GPT-5.5 retains decisive advantages in DeepSWE, Terminal-Bench, the Codex ecosystem, and omnimodal input. For teams building production coding agents where infrastructure maturity and enterprise compliance justify the premium, GPT-5.5 remains the safer choice. But the gap is shrinking — and at 6.8× the cost, the premium is getting harder to justify.

Sources & Links

• VentureBeat — Z.ai's GLM-5.2 beats GPT-5.5 on long-horizon benchmarks
• MarkTechPost — GLM-5.2 launch details
• DataCamp — GLM-5.2 features, setup, and model switching guide
• Lushbinary — GLM-5.2 API pricing and GLM Coding Plan guide
• Digital Applied — GLM-5.2 lands on Z.ai's coding plan
• Reddit r/chutesAI — GLM-5.2 community discussion
• AICodeKing (129K) — GLM-5.2 fully tested
• Vellum — GPT-5.5 benchmarks
• Vals.ai — SWE-bench Verified leaderboard
• Nipralo — GPT-5.5 review 2026
• Tosea — Codex complete guide 2026