Mistral Large 4 vs Particle-1
Mistral AI · France | Particle AI · US · Updated June 2026
Quick verdict
Pick Mistral Large 4 for mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder or trained from scratch on 3,800 nvidia grace blackwell gpus in mistral's own european data centers, across 160+ languages. 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 Mistral Large 4 if you need self-hosting or data privacy; Particle-1 if you want a managed API.
Mistral Large 4 (Mistral AI, France) 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. Mistral Large 4 is mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. 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 2.8× cheaper on input ($0.25/$1 per 1M tokens vs $0.7/$2.1 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: 1M tokens 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 22 days (released October 28, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a France-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Mistral Large 4 | Particle-1 |
|---|---|---|
| Provider | Mistral AI (France) | Particle AI (US) |
| Released | October 6, 2026 | October 28, 2026 |
| Context window | 1M tokens (~1,500 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $0.7/$2.1 per 1M tokens | $0.25/$1 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder
Mistral Large 4
Particle-1 is comparatively weak here — aPI-only with no published weights, although its DeepSeek base model is open
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Particle-1 is comparatively weak here — 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
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10 — 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
Mistral Large 4 is comparatively weak here — launched as a public preview — Mistral's launch post said the model was still improving
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
Mistral Large 4 is comparatively weak here — scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark
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 runs cheaper at $0.25/$1 per 1M tokens.
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 Mistral Large 4, 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
→ Mistral Large 4
Open weights let you run it on your own hardware; Particle-1 is API-only.
Anyone whose priority is mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder
→ Mistral Large 4
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 Mistral Large 4
Origin (France vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Mistral Large 4: where it fits
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages, hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning, and open weights on Hugging Face since October 29, 2026, with an NVFP4 checkpoint for Blackwell GPUs, making it among the largest open-weight models available.
Its trade-offs are real: launched as a public preview — Mistral's launch post said the model was still improving, mistral Ecosystem Open-Weight License is a modified MIT license: selling hosted access to third parties needs a separate commercial license, self-hosting a 1T-parameter MoE needs datacenter-class GPUs, and scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark. At $0.7 in / $2.1 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. Mistral Large 4 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 Mistral Large 4 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 Mistral Large 4 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, Mistral Large 4 leans toward mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder 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, Mistral Large 4 or Particle-1?
Mistral Large 4 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 tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Mistral Large 4 and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Mistral Large 4, 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, Mistral Large 4 or Particle-1?
Particle-1 — released October 28, 2026, about 22 days after Mistral Large 4.
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.