GLM 5.2 vs GPT-5.5: Choosing a Coding Model for Your Team

Last updated: October 7, 2026

GPT-5.5 from OpenAI dominates public coding benchmarks. GLM 5.2 from Zhipu Z.ai enters the conversation with MIT-licensed weights, a million-token context window, and a pricing model that undercuts the premium tier significantly.

These represent the cleanest version of the open-versus-closed tradeoff in coding models. Here's how to think through the choice for your engineering team.

The Core Tradeoff

GPT-5.5 is the known quantity. It scores over 85 percent on LiveCodeBench, mid-to-high 70s on SWE-bench validated with standard scaffolding, and consistently tops coding and reasoning indexes. Independent benchmarks have been published across thousands of evaluations over roughly eighteen months. Whatever your specific workload, there's probably a number published close to it.

GLM 5.2 launched June 13, 2026, under MIT license with weights available shortly after. Its predecessor, GLM 5.1, posted strong numbers on SWE-Bench Pro and Terminal-Bench 2.0, but independent verification of GLM 5.2's performance across a range of coding tasks is still rolling out.

The honest reading: GPT-5.5 is proven. GLM 5.2 is promising but unproven at scale.

Context Window Reality Check

Both models nominally support a 1 million token context—but with caveats.

GPT-5.5's 1 million token layer is available for higher-tier API access and certain product surfaces. Standard context is 256K. Using the full million at standard pricing runs expensive: an example calculation for 800K input alone costs $4 before output charges. Most teams limit context to 200K-400K to manage costs.

GLM 5.2's 1 million token context is the default across all GLM Coding Plan tiers. Z.ai calls it "usable"—meaning the model demonstrably maintains understanding across the full input. Within the coding plan subscription, reaching that ceiling doesn't incur extra charges.

If your team builds agents that operate across entire repositories, the context economics favor GLM 5.2.

The Token Economy

This is where the gap widens.

GPT-5.5 runs $5 input and $30 output per million tokens at standard pricing—among the most expensive frontier models available. A typical agent coding run generating 200K tokens in combined input, tool calls, and reasoning lands at roughly $6-8. Multiply that by daily use across a team and costs accumulate fast.

GLM 5.2 in the coding plan is a flat monthly subscription. Heavy individual usage doesn't increase per-engineer costs. When the standalone API launched, pricing was expected in the $1-2 input and $3-6 output range based on GLM 5.1 rates—roughly 5-10 times cheaper for agent runs.

Our Analysis: When to Pay the Premium

The math is straightforward for teams running heavy inference.

Organizations with substantial monthly inference bills face a genuine decision. Even accepting a meaningful quality tradeoff with GLM 5.2 can be justified by dramatic cost savings if your team values the time freed up from budget constraints. For smaller operations, the cost gap exists but isn't decisive—quality and reliability matter more than savings at that scale.

The harder question is evaluation cost. Running meaningful A/B tests between models requires setting up evals, establishing baselines, and running parallel experiments. That investment itself isn't free. For teams already running against GPT-5.5 who can't tolerate any quality regression in their current eval suite, the right move is to wait for independent GLM 5.2 benchmarks before piloting.

Practical Example: Evaluating for Repo-Scale Agents

Say your team is building agents that need to understand large codebases—multiple services, shared libraries, years of history. You want to pass the full context to the model rather than chunking.

With GPT-5.5, you're paying premium rates for the 1M context and likely capping at smaller windows to control the bill. With GLM 5.2, the full 1M context is available by default with no per-token penalty in the coding plan.

If your team's primary use case is repo-scale comprehension and code generation, GLM 5.2's economics are compelling. If you're running conversational coding assistance where context rarely exceeds typical working sizes, the cost difference is mostly theoretical.

Multimodal and Self-Hosting Considerations

Need vision or audio? GPT-5.5 handles text, code, images, and audio. GLM 5.2 processes text and code only—Zhipu has a separate vision line for image inputs.

On self-hosting, GLM 5.2's MIT weights open the door to air-gapped deployments. GPT-5.5 is API-only, meaning your code and prompts go to OpenAI servers. For regulated industries with strict data residency requirements, that distinction settles the debate immediately.

Bottom Line

GPT-5.5 is the right choice if you need proven performance, have the budget, and your use case doesn't require extremely large contexts. GLM 5.2 is worth evaluating if cost optimization matters, you need the full 1M context by default, or you're building self-hosted solutions. Watch for independent benchmarks dropping 1-2 weeks after the API and open weights fully release.

Key takeaways

• GPT-5.5 leads on public benchmarks; GLM 5.2 leads on cost and context flexibility
• GLM 5.2's 1M context is the default with no per-token charges in the coding plan
• Teams running heavy inference should run the math on GLM 5.2 against their current costs
• Wait for independent benchmarks before piloting if you can't tolerate any quality regression
• For regulated industries requiring data control, GLM 5.2's open weights may be the only viable path