GLM 5 vs GPT-4.1 Mini
Z.ai · China | OpenAI · US · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick GPT-4.1 Mini for very cheap high-volume text work at $0.40 in / $1.60 out per million tokens or instruction following above its weight class — 84.1% on ifeval, beating gpt-4o. Choose GLM 5 if you need self-hosting or data privacy; GPT-4.1 Mini if you want a managed API.
GLM 5 (Z.ai, China) and GPT-4.1 Mini (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 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. GPT-4.1 Mini is a cheap, fast 1M-context workhorse with strong instruction following but weak coding — already retired from ChatGPT. They diverge most on price, context window, open vs. closed weights and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: GPT-4.1 Mini is about 2.5× cheaper on input ($0.4/$1.6 per 1M tokens vs $1/$3.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: GPT-4.1 Mini holds 5.2× more — 1M (~1,571 pages) vs 200K (~300 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 5 leads SWE-Bench Verified by 54.2 points (77.8% vs 23.6%) — a real edge on hard, real-world software tasks.
- ▸Recency: GLM 5 is the newer model by about 10 months (released February 12, 2026), 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 5 | GPT-4.1 Mini |
|---|---|---|
| Provider | Z.ai (China) | OpenAI (US) |
| Released | February 12, 2026 | April 14, 2025 |
| Context window | 200K (~300 pages) | 1M (~1,571 pages) |
| Price (in/out) | $1/$3.2 per 1M tokens | $0.4/$1.6 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | 77.8% | 23.6% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
It scores 77.8% on SWE-Bench Verified against GPT-4.1 Mini's 23.6% — a 54.2-point edge on real repository work.
Complex systems design and backend reasoning
GLM 5
GPT-4.1 Mini is comparatively weak here — a June 2024 knowledge cutoff, now roughly two years stale, and no reasoning mode
Iterative self-correction on autonomous tasks
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it leads SWE-Bench Verified 77.8% to 23.6%.
Very cheap high-volume text work at $0.40 in / $1.60 out per million tokens
GPT-4.1 Mini
At $0.4/$1.6 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.
Instruction following above its weight class — 84.1% on IFEval, beating GPT-4o
GPT-4.1 Mini
A cheap, fast 1M-context workhorse with strong instruction following but weak coding — already retired from ChatGPT — and it runs cheaper at $0.4/$1.6 per 1M tokens.
Multi-turn coherence for its tier — 35.8% on MultiChallenge, roughly 1.8x GPT-4o mini
GPT-4.1 Mini
A cheap, fast 1M-context workhorse with strong instruction following but weak coding — already retired from ChatGPT — and it carries the larger 1M context.
Lowest cost at scale
GPT-4.1 Mini
At $0.4/$1.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-4.1 Mini
Its 1M window is about 5.2× larger than GLM 5's 200K, fitting roughly 1,571 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-4.1 Mini
At $0.4/$1.6 per 1M tokens it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-4.1 Mini
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ GLM 5
Open weights let you run it on your own hardware; GPT-4.1 Mini is API-only.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is very cheap high-volume text work at $0.40 in / $1.60 out per million tokens
→ GPT-4.1 Mini
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-4.1 Mini or GLM 5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
GPT-4.1 Mini: where it fits
A cheap, fast 1M-context workhorse with strong instruction following but weak coding — already retired from ChatGPT. Released April 14, 2025 by OpenAI, it is built for very cheap high-volume text work at $0.40 in / $1.60 out per million tokens, instruction following above its weight class — 84.1% on IFEval, beating GPT-4o, multi-turn coherence for its tier — 35.8% on MultiChallenge, roughly 1.8x GPT-4o mini, and a full 1M context at flat pricing, with no long-context premium.
Its trade-offs: weak at agentic coding — its 23.6% on SWE-Bench Verified sits below GPT-4o's 33.2%, retired from ChatGPT in February 2026, and OpenAI's own docs now point users to GPT-5 mini instead, and a June 2024 knowledge cutoff, now roughly two years stale, and no reasoning mode. At $0.4 in / $1.6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-4.1 Mini gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GLM 5 and GPT-4.1 Mini 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 5 or GPT-4.1 Mini better for coding?
On SWE-Bench Verified, GLM 5 scores 77.8% and GPT-4.1 Mini scores 23.6% — GLM 5 has the measurable edge.
Which is cheaper, GLM 5 or GPT-4.1 Mini?
GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-4.1 Mini is API-metered at $0.4/$1.6 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-4.1 Mini — 1M vs 200K, about 5.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and GPT-4.1 Mini together?
Yes — a multi-model platform like LumiChats gives you GLM 5, GPT-4.1 Mini 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 5 or GPT-4.1 Mini?
GLM 5 — released February 12, 2026, about 10 months after GPT-4.1 Mini.
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.