GPT-5.6 Luna vs GLM 5.2
Two models sit close on headline coding scores, but they make very different trade-offs. GPT-5.6 Luna is a fast proprietary model with image input, a broad hosted-tool surface, and OpenAI's published frontier evaluation suite. GLM 5.2 is an MIT-licensed, open-weight long-horizon model with a solid 1M context and lower output pricing.
At a glance
| Specification | GPT-5.6 Luna | GLM 5.2 |
|---|---|---|
| Provider / release | OpenAI · generally available July 2026 | Z.AI · June 2026 |
| API model ID | gpt-5.6-luna | glm-5.2 |
| Context window | 1.05M tokens | 1M tokens |
| Maximum output | 128K tokens | 128K tokens |
| Input / output | Text and images / text | Text / text |
| Reasoning controls | High reasoning tier in the GPT-5.6 family | Thinking enabled with High or Max effort |
| Function calling / structured output | Supported; hosted web, file, computer and code tools | Supported; MCP, function calling, caching and structured output |
| Weights / license | Closed, proprietary API | Open weights, MIT license |
| Cache-read price | $0.10 / 1M tokens | $0.26 / 1M tokens |
The parameter-count comparison needs a footnote. Z.AI's official GLM-5.2 material focuses on its architecture and evaluation setup rather than a single headline count; public model trackers commonly report roughly 744B–753B total parameters and about 40B active parameters. Those figures are useful deployment context, but they should not be treated as an official replacement for the model card.
Benchmark scoreboard: a close Pro result, a wider long-horizon gap
The numbers below preserve each provider's published evaluation lane. Luna's figures come from OpenAI's GPT-5.6 scorecard. GLM 5.2's figures come from Z.AI's official benchmark table. “Winner” is descriptive, not a claim that the two runs are perfectly apples-to-apples: scaffolds, prompts, context limits, sampling and timeouts differ.
| Evaluation | Luna | GLM 5.2 | Reading |
|---|---|---|---|
| SWE-Bench Pro | 62.7% | 62.1% | Luna +0.6 · effectively close |
| DeepSWE v1.1 | 67.2% | 46.2% | Luna +21.0 · harness-sensitive |
| Terminal-Bench 2.1 | 84.7% | 81.0% | Luna +3.7 · GLM Terminus-2 lane |
| GPQA Diamond | 92.3% | 91.2% | Luna +1.1 · close |
| Toolathlon / Tool-Decathlon | 53.4% | 48.2% | Different named evaluation variants |
| MCP Atlas public | Not published by OpenAI | 76.8% | Do not infer a Luna loss |
| HLE with tools | Not in Luna's official table | 54.7% | Unmatched evidence |
Radar: capability evidence, not a universal ranking
● Luna ● GLM 5.2
The radar uses four shared published metrics and maps their native percentage scores onto the same 0–100 visual scale. It deliberately excludes unmatched MCP and HLE rows.
- Luna has the visible edge on the selected coding rows, especially DeepSWE.
- GLM 5.2 remains close on SWE Pro and GPQA while adding open deployment rights.
- Neither polygon captures latency, token consumption, reliability, or your tool harness.
Architecture and workflow fit
GPT-5.6 Luna: a hosted production tier
Luna is the cost-efficient member of OpenAI's GPT-5.6 family. It shares the family's 1.05M-token window and 128K output ceiling and accepts text or image input. Its advantage is not only the base model: OpenAI exposes a mature Responses API tool surface, including web search, file search, code interpreter, hosted shell, computer use, MCP, tool search and structured outputs.
That makes Luna attractive for teams that want one managed API, predictable safety controls, and a route from routine work to stronger GPT-5.6 tiers without operating a trillion-parameter model.
GLM 5.2: long-horizon openness
GLM 5.2 was designed around long engineering trajectories. Z.AI highlights a solid 1M context, flexible High/Max thinking effort, IndexShare for sparse attention, speculative-decoding improvements, and training on project-scale coding-agent tasks. It supports MCP, function calls, caching and structured output.
The MIT license and public weights change the deployment conversation: teams can inspect, fine-tune or self-host, subject to the very real hardware and operations burden of a roughly 744B–753B model.
| Workflow question | Better default | Why |
|---|---|---|
| Need image understanding in the same model? | Luna | GLM 5.2's cited API is text-in/text-out. |
| Need a permissive open license? | GLM 5.2 | MIT weights versus Luna's proprietary API. |
| Need the broadest managed tool ecosystem? | Luna | OpenAI documents hosted tools and tool search. |
| Need 1M-token project context? | Near tie | 1.05M Luna versus 1M GLM; validate retrieval quality. |
| Need lower output-token pricing? | GLM 5.2 | $4.40 versus Luna's $6 per 1M output tokens. |
Pricing: the answer changes with the input/output mix
At standard short-context rates, Luna is cheaper on fresh input but GLM 5.2 is cheaper on generated output. That makes neither model the universal cost winner.
| Per 1M tokens | GPT-5.6 Luna | GLM 5.2 |
|---|---|---|
| Fresh input | $1.00 | $1.40 |
| Cached input | $0.10 | $0.26 |
| Output | $6.00 | $4.40 |
| Long-context surcharge | Above 272K: $2 input / $9 output | Provider pricing varies by route; confirm current rate |
In that input-heavy example Luna costs about 13% less. Reverse the mix to 10M input plus 5M output and the totals become $40 for Luna versus $36 for GLM 5.2. For a coding agent that emits a lot of reasoning and patch text, GLM's lower output rate can matter more than its higher input rate. For repeated prompts, cache-hit behavior and actual token counts dominate the simple rate card.
Who should choose which?
Choose GPT-5.6 Luna if…
- You need image input, hosted browsing, file search, computer use or a broad OpenAI tool chain.
- You value the strongest published Luna coding profile: 62.7% SWE-Bench Pro, 67.2% DeepSWE v1.1 and 84.7% Terminal-Bench 2.1 in OpenAI's scorecard.
- You want a managed API and the option to escalate within the GPT-5.6 family.
- You are willing to pay a premium for a proprietary service and do not need weight access.
Choose GLM 5.2 if…
- MIT licensing, open weights, or self-hosting is a strategic requirement.
- Your workload is long-horizon text coding and the 1M context is genuinely used.
- Output tokens dominate your bill; $4.40/M is below Luna's $6/M standard output rate.
- You need flexible thinking effort and are prepared to validate the model with your own agent harness.
Run the comparison on your own code
Public benchmarks describe a model. Your repository, tool schema and latency budget decide whether it is the right model.
Try both in CodingFleet →Bottom line
GPT-5.6 Luna is the safer managed default. It has a slightly higher SWE-Bench Pro score, a much stronger published DeepSWE result, image input and a broad, integrated tool surface. It is particularly compelling for production teams that want to route work inside OpenAI's ecosystem.
GLM 5.2 is the more open and long-horizon-oriented alternative. It brings MIT weights, 1M context, flexible effort control and a lower output price. Its coding scores are close enough on SWE Pro that licensing and deployment requirements can easily outweigh a small benchmark delta.
The honest conclusion is not “Luna wins” or “GLM wins.” It is: Luna wins the managed capability package; GLM 5.2 wins the openness and output-economics package. Test the same prompts, same scaffold and same budget before committing.
Sources and methodology
- OpenAI — GPT-5.6: Frontier intelligence that scales with your ambition. Primary source for Luna pricing, capabilities and benchmark rows.
- OpenAI API — GPT-5.6 Luna and OpenAI API pricing.
- Z.AI — GLM-5.2: Built for Long-Horizon Tasks. Primary source for GLM 5.2 context, architecture and benchmark table.
- Z.AI developer documentation — GLM-5.2 and Z.AI pricing.
- Provider-reported scores use different scaffolds, effort levels, context limits and evaluation dates. The radar and bars are visual aids, not a statistically weighted overall ranking. Unpublished scores are shown as “not published,” never as zero.