Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs gpt-oss-120b
gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
GLM-5.3Z.ai (Zhipu AI) · Open-weight model
B64.6/100#17 of 100 open-weight models
gpt-oss-120bOpenAI · Open-weight model
BB70.2/100#6 of 100 open-weight models
Which one, for what
GLM-5.3 B
Best for Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider
Ahead on
- Capability, 80 against 54
- Documentation and upkeep, 58 against 45
Watch for
Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build
gpt-oss-120b BB
Best for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support
Ahead on
- Reach, 75 against 55
- Risk, 95 against 50
- Agent interface, 90 against 70
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5
Watch for
Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision
Score by category
| Category | Weight this run | GLM-5.3 | gpt-oss-120b | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 80 | 54 | GLM-5.3 +26 |
| Reach | 25%25 | 55 | 75 | gpt-oss-120b +20 |
| Risk | 20%20 | 50 | 95 | gpt-oss-120b +45 |
| Agent interface | 10%10 | 70 | 90 | gpt-oss-120b +20 |
| Documentation and upkeep | 10%10 | 58 | 45 | GLM-5.3 +13 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.6/100 | BB 70.2/100 |
Facts side by side
| Fact | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | OpenAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2025-08-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.8/5 (4) | 3.5/5 (4) |
Verdicts
GLM-5.3
A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.
gpt-oss-120b
A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.
Before you call either
GLM-5.3
- Start it with
vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3(vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe) - Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
- Pass
chat_template_kwargs: {"reasoning_effort": "low"}for short tasks; anything other than low or high falls back to max - Pass
clear_thinking: truein chat use, since the template keeps earlier reasoning by default - Don't send
enable_thinking: false; the template ignores it and always opens a think block - Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
- No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe
gpt-oss-120b
- Start it with
ollama run gpt-oss:120bon the reference machine, the build that fits - On the Strix Halo machine raise the GPU memory limit first with
amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs - With vLLM use
vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choiceper vLLM's recipe, or parse raw output with openai-harmony - Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
- Set
Reasoning: lowin the system message for short tasks, medium is the default - Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
- Expect one tool call per turn and loop, don't wait for parallel calls
Questions
Which is better for AI agents, GLM-5.3 or gpt-oss-120b?
gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep.
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- BigBang-v1 vs GLM-5.3
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- Cosmos-Reason2-2B vs GLM-5.3
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- Darwin-36B-Opus vs GLM-5.3
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- Gemma 4 26B A4B IT vs gpt-oss-120b
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- Gemma 4 E2B IT vs GLM-5.3
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- GLM-4.5-Air vs GLM-5.3
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- GLM-5.3 vs GLM-5.3-Flash
- GLM-5.3 vs gpt-oss-20b
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- GLM-5.3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-5.3 vs Kimi K3
- GLM-5.3 vs Kolibri-1
- GLM-5.3 vs Laguna M.1
- GLM-5.3 vs Laguna S 2.1
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- GLM-5.3 vs MiniCPM5-2B
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- GLM-5.3 vs Muse Glimmer 30B
- GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B
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- gpt-oss-120b vs Muse Glimmer 30B
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- gpt-oss-120b vs Qwen3.8-27B
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- gpt-oss-120b vs Qwen3-Coder-Next
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- gpt-oss-120b vs Swift 1.5 Qwen3.8-27B
- gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next
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Machine-readable
- This page as Markdown
/compare/glm-5-3-vs-gpt-oss-120b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-5-3.json·/api/v1/tools/gpt-oss-120b.json - From a terminal
anchor compare glm-5-3 gpt-oss-120b(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "gpt-oss-120b"}at/mcp, no key