GLM 5 vs Particle-1
Z.ai · China | Particle AI · 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 Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash or particle reports 72.57% task pass rate versus 71.15% for deepseek v4.1 flash, at $0.2211 versus $0.3368 per task. Choose GLM 5 if you need self-hosting or data privacy; Particle-1 if you want a managed API.
GLM 5 (Z.ai, China) and Particle-1 (Particle AI, 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. Particle-1 is particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Particle-1 is about 4× cheaper on input ($0.25/$1 per 1M tokens vs $1/$3.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: Particle-1 holds 5.2× more — 1M tokens (~1,573 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.
- ▸Recency: Particle-1 is the newer model by about 9 months (released October 28, 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 | Particle-1 |
|---|---|---|
| Provider | Z.ai (China) | Particle AI (US) |
| Released | February 12, 2026 | October 28, 2026 |
| Context window | 200K (~300 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $1/$3.2 per 1M tokens | $0.25/$1 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | 77.8% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and its weights are open while Particle-1 is API-only.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; Particle-1 does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Particle-1 does not.
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
Particle-1
Its 1M tokens window holds about 5.2× more than GLM 5's 200K in a single prompt.
Particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task
Particle-1
Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates — and it runs cheaper at $0.25/$1 per 1M tokens.
Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks
Particle-1
Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates — and it carries the larger 1M tokens context.
Lowest cost at scale
Particle-1
At $0.25/$1 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Particle-1
Its 1M tokens window is about 5.2× larger than GLM 5's 200K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Particle-1
At $0.25/$1 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
→ Particle-1
Larger 1M tokens 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; Particle-1 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 token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash
→ Particle-1
That is its strongest area.
An enterprise with regional data-residency rules
→ Particle-1 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.
Particle-1: where it fits
Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. Released October 28, 2026 by Particle AI, it is built for token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash, particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task, particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks, and peak API price of $0.25/$1.00 per million tokens, halved off-peak to $0.125/$0.50 (20:30-04:00 New York time).
Its trade-offs: aPI-only with no published weights, although its DeepSeek base model is open, all benchmarks are vendor-reported on Particle's own harness; none were independently reproduced as of October 31, and pricing is inconsistent across sources: Particle's launch post said $0.30/$1.20 and OpenRouter shows $0.05/$0.50, while its billing docs say $0.25/$1.00. At $0.25 in / $1 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. Particle-1 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 Particle-1 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 Particle-1 better for coding?
Public SWE-Bench figures are not available for Particle-1, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows while Particle-1 leans toward token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Particle-1?
GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Particle-1 is API-metered at $0.25/$1 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?
Particle-1 — 1M tokens 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 Particle-1 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Particle-1 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 Particle-1?
Particle-1 — released October 28, 2026, about 9 months after GLM 5.
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.