Inkling vs Particle-1
Thinking Machines Lab · US | Particle AI · US · Updated June 2026
Quick verdict
Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. 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 Inkling if you need self-hosting or data privacy; Particle-1 if you want a managed API.
Inkling (Thinking Machines Lab) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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
- ▸Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Particle-1 is API-metered at $0.25/$1 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: 1M vs 1M tokens — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Particle-1 is the newer model by about 4 months (released October 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Inkling | Particle-1 |
|---|---|---|
| Provider | Thinking Machines Lab (US) | Particle AI (US) |
| Released | July 15, 2026 | October 28, 2026 |
| Context window | 1M (~1,500 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.25/$1 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, audio, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Open weights make this possible at all — Particle-1 is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Particle-1 is comparatively weak here — aPI-only with no published weights, although its DeepSeek base model is open
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and its weights are open while Particle-1 is API-only.
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
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 is the newer of the two.
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-1 lists particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task among its strengths; Inkling does not.
Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks
Particle-1
Particle-1 lists particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks among its strengths; Inkling does not.
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of Particle-1's $0.25/$1 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) it undercuts Particle-1, 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
→ Inkling
Open weights let you run it on your own hardware; Particle-1 is API-only.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
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.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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. Inkling 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 Inkling 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 Inkling or Particle-1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai 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, Inkling or Particle-1?
Inkling 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?
Effectively neither — 1M vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Inkling and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Particle-1?
Particle-1 — released October 28, 2026, about 4 months after Inkling.
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.