Mistral Large 4 vs Muse Spark 1.1
Mistral AI · France | Meta · US · Updated June 2026
Quick verdict
Pick Mistral Large 4 for mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), 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 Muse Spark 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) or subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck. On a tight budget at scale, Mistral Large 4 is the value pick.
Mistral Large 4 (Mistral AI, France) and Muse Spark 1.1 (Meta, 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 October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. Muse Spark 1.1 is meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Mistral Large 4 is about 1.8× cheaper on input ($0.68/$2.09 per 1M tokens vs $1.25/$4.25 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: 1M tokens vs 1M — 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: Mistral Large 4 is the newer model by about 3 months (released October 6, 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 | Muse Spark 1.1 |
|---|---|---|
| Provider | Mistral AI (France) | Meta (US) |
| Released | October 6, 2026 | July 9, 2026 |
| Context window | 1M tokens (~1,500 pages) | 1M (~1,573 pages) |
| Price (in/out) | $0.68/$2.09 per 1M tokens | $1.25/$4.25 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image, video, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | 54.1% |
Who wins what
Mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), plus a 1.6B-parameter vision encoder
Mistral Large 4
Muse Spark 1.1 is comparatively weak here — closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end — and it runs cheaper at $0.68/$2.09 per 1M tokens.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end — and it is the newer of the two.
Scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported)
Muse Spark 1.1
Muse Spark 1.1 lists scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported) among its strengths; Mistral Large 4 does not.
Subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck
Muse Spark 1.1
Muse Spark 1.1 lists subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck among its strengths; Mistral Large 4 does not.
Professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported)
Muse Spark 1.1
Muse Spark 1.1 lists professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported) among its strengths; Mistral Large 4 does not.
Lowest cost at scale
Mistral Large 4
At $0.68/$2.09 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
→ Mistral Large 4
At $0.68/$2.09 per 1M tokens it undercuts Muse Spark 1.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Muse Spark 1.1
Larger 1M window fits more in one prompt.
Anyone whose priority is mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder
→ Mistral Large 4
It is specifically built for that.
Anyone whose priority is scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported)
→ Muse Spark 1.1
That is its strongest area.
An enterprise with regional data-residency rules
→ Muse Spark 1.1 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 October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), 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 planned by end of October 2026, which would make it among the largest open-weight models available.
Its trade-offs are real: still a public preview, not a finished release — Mistral says training is ongoing with 'no signs of saturation', weights not yet shipped as of mid-October 2026 — API-only for now despite the open-weight framing, launch pricing ($0.68/$2.09 per million) is a preview discount — Mistral's own model page lists a $1.36/$4.18 list price once the promotion ends, 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 $0.68 in / $2.09 out per million tokens, it sits in the budget price band.
Muse Spark 1.1: where it fits
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Released July 9, 2026 by Meta, it is built for scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported), subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck, professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported), and managing its own context: it compacts the 1M window mid-run instead of relying on external windowing.
Its trade-offs: not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark, the 1M window oversells its recall: 54.1 on MRCR v2 at 1M against GPT-5.5's 74.0, closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for, and uS-only public preview behind a waitlist, and every benchmark is vendor-reported with no third-party replication. At $1.25 in / $4.25 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Mistral Large 4 (France) and Muse Spark 1.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Large 4 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Mistral Large 4 and Muse Spark 1.1 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 Muse Spark 1.1 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 — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder while Muse Spark 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Large 4 or Muse Spark 1.1?
Mistral Large 4 is cheaper — $0.68/$2.09 per 1M tokens vs $1.25/$4.25 per 1M tokens, roughly 1.8× apart on input.
Which has the bigger context window?
Effectively neither — 1M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Mistral Large 4 and Muse Spark 1.1 together?
Yes — a multi-model platform like LumiChats gives you Mistral Large 4, Muse Spark 1.1 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 Muse Spark 1.1?
Mistral Large 4 — released October 6, 2026, about 3 months after Muse Spark 1.1.
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.