GLM 4.7 vs gpt-oss-120b
Z.ai · China | OpenAI · US · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). On a tight budget at scale, gpt-oss-120b is the value pick.
GLM 4.7 (Z.ai, China) and gpt-oss-120b (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. They diverge most on price, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: GLM 4.7 holds 1.5× more — 200K (~304 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Coding: GLM 4.7 leads SWE-Bench Verified by 11.4 points (73.8% vs 62.4%) — a real edge on hard, real-world software tasks.
- ▸Recency: GLM 4.7 is the newer model by about 5 months (released December 22, 2025), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | GLM 4.7 | gpt-oss-120b |
|---|---|---|
| Provider | Z.ai (China) | OpenAI (US) |
| Released | December 22, 2025 | August 5, 2025 |
| Context window | 200K (~304 pages) | 131K (~197 pages) |
| Price (in/out) | $0.6/$2.2 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 73.8% | 62.4% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it leads SWE-Bench Verified 73.8% to 62.4%.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
It scores 73.8% on SWE-Bench Verified against gpt-oss-120b's 62.4% — a 11.4-point edge on real repository work.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
Its 200K window holds about 1.5× more than gpt-oss-120b's 131K in a single prompt.
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
gpt-oss-120b lists self-hostable on a single 80GB H100 GPU via MXFP4 among its strengths; GLM 4.7 does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; GLM 4.7 does not.
Agentic tool use, function calling, and code execution
gpt-oss-120b
gpt-oss-120b lists agentic tool use, function calling, and code execution among its strengths; GLM 4.7 does not.
Lowest cost at scale
gpt-oss-120b
Its weights are open, so at volume you pay for your own hardware instead of GLM 4.7's $0.6/$2.2 per 1M tokens.
Largest single-prompt input
GLM 4.7
Its 200K window is about 1.5× larger than gpt-oss-120b's 131K, fitting roughly 304 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ gpt-oss-120b
At Open weight (self-host / free) it undercuts GLM 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 4.7
Larger 200K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
It is specifically built for that.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
That is its strongest area.
An enterprise with regional data-residency rules
→ gpt-oss-120b or GLM 4.7
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget price band.
gpt-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GLM 4.7 (China) and gpt-oss-120b (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. gpt-oss-120b is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both GLM 4.7 and gpt-oss-120b without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is GLM 4.7 or gpt-oss-120b better for coding?
On SWE-Bench Verified, GLM 4.7 scores 73.8% and gpt-oss-120b scores 62.4% — GLM 4.7 has the measurable edge.
Which is cheaper, GLM 4.7 or gpt-oss-120b?
gpt-oss-120b is cheaper — $0.6/$2.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
GLM 4.7 — 200K vs 131K, about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and gpt-oss-120b together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, gpt-oss-120b and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 4.7 or gpt-oss-120b?
GLM 4.7 — released December 22, 2025, about 5 months after gpt-oss-120b.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.