Claude Opus 4.6 vs Particle-1
Anthropic · US | Particle AI · US · Updated June 2026
Quick verdict
Pick Claude Opus 4.6 for agentic coding and debugging in large codebases or long-running, multi-step autonomous agent tasks. 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. On a tight budget at scale, Particle-1 is the value pick.
Claude Opus 4.6 (Anthropic) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Claude Opus 4.6 is anthropic's February 2026 flagship Opus and the first Opus-class model with a 1M-token context window, built for agentic coding and long-running professional tasks. 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Particle-1 is about 20× cheaper on input ($0.25/$1 per 1M tokens vs $5/$25 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸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 9 months (released October 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Claude Opus 4.6 | Particle-1 |
|---|---|---|
| Provider | Anthropic (US) | Particle AI (US) |
| Released | February 5, 2026 | October 28, 2026 |
| Context window | 1M (~1,500 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $5/$25 per 1M tokens | $0.25/$1 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | 80.8% | Not published |
| MRCR v2 @ 1M | 76% | Not published |
Who wins what
Agentic coding and debugging in large codebases
Claude Opus 4.6
Claude Opus 4.6 lists agentic coding and debugging in large codebases among its strengths; Particle-1 does not.
Long-running, multi-step autonomous agent tasks
Claude Opus 4.6
Claude Opus 4.6 lists long-running, multi-step autonomous agent tasks among its strengths; Particle-1 does not.
Frontier multidisciplinary reasoning (leads Humanity's Last Exam)
Claude Opus 4.6
Claude Opus 4.6 lists frontier multidisciplinary reasoning (leads Humanity's Last Exam) among its strengths; Particle-1 does not.
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
Particle-1
Claude Opus 4.6 is comparatively weak here — superseded by newer Claude Opus 4.7 and 4.8 (now a legacy model)
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 is the newer of the two.
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Particle-1
At $0.25/$1 per 1M tokens it undercuts Claude Opus 4.6, 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.
Anyone whose priority is agentic coding and debugging in large codebases
→ Claude Opus 4.6
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.
Claude Opus 4.6: where it fits
Anthropic's February 2026 flagship Opus and the first Opus-class model with a 1M-token context window, built for agentic coding and long-running professional tasks. Released February 5, 2026 by Anthropic, it is built for agentic coding and debugging in large codebases, long-running, multi-step autonomous agent tasks, frontier multidisciplinary reasoning (leads Humanity's Last Exam), and economically valuable knowledge work in finance and legal (GDPval-AA).
Its trade-offs are real: superseded by newer Claude Opus 4.7 and 4.8 (now a legacy model), and top-tier per-token price, and its 1M-token context shipped as beta. At $5 in / $25 out per million tokens, it sits in the premium 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
Claude Opus 4.6 and Particle-1 overlap enough that the right pick depends on your specific job. Particle-1 costs less per token; Particle-1 holds the larger context; and each leads in its own area — Claude Opus 4.6 for agentic coding and debugging in large codebases, Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Claude Opus 4.6 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 Claude Opus 4.6 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, Claude Opus 4.6 leans toward agentic coding and debugging in large codebases 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, Claude Opus 4.6 or Particle-1?
Particle-1 is cheaper — $5/$25 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 20× apart on input.
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 Claude Opus 4.6 and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Claude Opus 4.6, 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, Claude Opus 4.6 or Particle-1?
Particle-1 — released October 28, 2026, about 9 months after Claude Opus 4.6.
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.