Mercury 2.5 Preview vs Particle-1
Inception Labs · US | Particle AI · US · Updated June 2026
Quick verdict
Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). 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, Mercury 2.5 Preview is the value pick.
Mercury 2.5 Preview (Inception Labs) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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: Mercury 2.5 Preview is about 6.3× cheaper on input ($0.04/$0.15 per 1M tokens vs $0.25/$1 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: Particle-1 holds 4× more — 1M tokens (~1,573 pages) vs 260K tokens (~390 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 58 days (released October 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Mercury 2.5 Preview | Particle-1 |
|---|---|---|
| Provider | Inception Labs (US) | Particle AI (US) |
| Released | August 31, 2026 | October 28, 2026 |
| Context window | 260K tokens (~390 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $0.04/$0.15 per 1M tokens | $0.25/$1 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
Mercury 2.5 Preview
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it runs cheaper at $0.04/$0.15 per 1M tokens.
Coding accuracy (95.7%, 91st percentile among cost-optimized models)
Mercury 2.5 Preview
At $0.04/$0.15 per 1M tokens it undercuts Particle-1 ($0.25/$1 per 1M tokens), and that gap compounds at volume.
Mathematics accuracy (97.0%, 97th percentile)
Mercury 2.5 Preview
Mercury 2.5 Preview lists mathematics accuracy (97.0%, 97th percentile) 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 4× more than Mercury 2.5 Preview's 260K tokens 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
Mercury 2.5 Preview is comparatively weak here — positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class
Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks
Particle-1
Mercury 2.5 Preview is comparatively weak here — 260K context window is far shorter than frontier 1M-token models
Lowest cost at scale
Mercury 2.5 Preview
At $0.04/$0.15 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 4× larger than Mercury 2.5 Preview's 260K tokens, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mercury 2.5 Preview
At $0.04/$0.15 per 1M tokens 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.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
→ Mercury 2.5 Preview
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.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs are real: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 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
Mercury 2.5 Preview and Particle-1 overlap enough that the right pick depends on your specific job. Mercury 2.5 Preview costs less per token; Particle-1 holds the larger context; and each leads in its own area — Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, 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 Mercury 2.5 Preview 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 Mercury 2.5 Preview 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, Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation 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, Mercury 2.5 Preview or Particle-1?
Mercury 2.5 Preview is cheaper — $0.04/$0.15 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 6.3× apart on input.
Which has the bigger context window?
Particle-1 — 1M tokens vs 260K tokens, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Mercury 2.5 Preview and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Mercury 2.5 Preview, 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, Mercury 2.5 Preview or Particle-1?
Particle-1 — released October 28, 2026, about 58 days after Mercury 2.5 Preview.
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.