Fugu Ultra v2.0 vs Mistral Large 3

Sakana AI · Global  |  Mistral · France · Updated June 2026

Quick verdict

Pick Fugu Ultra v2.0 for sakana's own evaluation reports best-or-joint-best results on 5 of 8 benchmarks tested, including a 74.3 on the deepswe coding-repair benchmark or claims to outperform flagship models like gpt-6 astra and claude fable 5.1 despite its underlying orchestration pool reportedly excluding both of them. Pick Mistral Large 3 for open-weight (apache 2.0), self-hostable or strong multilingual performance. Choose Mistral Large 3 if you need self-hosting or data privacy; Fugu Ultra v2.0 if you want a managed API.

Fugu Ultra v2.0 (Sakana AI) and Mistral Large 3 (Mistral) are two of the models people most often weigh against each other in 2026. Fugu Ultra v2.0 is sakana AI's flagship multi-agent orchestration model, claiming best-or-joint-best results on 5 of 8 self-reported benchmarks against monolithic frontier models — $5/$30 per million tokens. Mistral Large 3 is france's frontier contender — strong multilingual model with European data residency. 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

Side-by-side specs

SpecFugu Ultra v2.0Mistral Large 3
ProviderSakana AI (Global) Mistral (France)
ReleasedSeptember 10, 2026 December 2, 2025
Context window1M tokens (~1,500 pages) 256K (~384 pages)
Price (in/out)$5/$30 per 1M tokens $0.5/$1.5 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Sakana's own evaluation reports best-or-joint-best results on 5 of 8 benchmarks tested, including a 74.3 on the DeepSWE coding-repair benchmark

Fugu Ultra v2.0

Mistral Large 3 is comparatively weak here — less benchmark coverage

Claims to outperform flagship models like GPT-6 Astra and Claude Fable 5.1 despite its underlying orchestration pool reportedly excluding both of them

Fugu Ultra v2.0

Sakana AI's flagship multi-agent orchestration model, claiming best-or-joint-best results on 5 of 8 self-reported benchmarks against monolithic frontier models — $5/$30 per million tokens — and it carries the larger 1M tokens context.

1M-token context window

Fugu Ultra v2.0

Its 1M tokens window holds about 3.9× more than Mistral Large 3's 256K in a single prompt.

Open-weight (Apache 2.0), self-hostable

Mistral Large 3

Open weights make this possible at all — Fugu Ultra v2.0 is API-only, so it cannot leave the vendor's servers.

Strong multilingual performance

Mistral Large 3

France's frontier contender — strong multilingual model with European data residency — and it runs cheaper at $0.5/$1.5 per 1M tokens.

Efficient inference

Mistral Large 3

France's frontier contender — strong multilingual model with European data residency — and its weights are open while Fugu Ultra v2.0 is API-only.

Lowest cost at scale

Mistral Large 3

At $0.5/$1.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Fugu Ultra v2.0

Its 1M tokens window is about 3.9× larger than Mistral Large 3's 256K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Mistral Large 3

At $0.5/$1.5 per 1M tokens it undercuts Fugu Ultra v2.0, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Fugu Ultra v2.0

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

Mistral Large 3

Open weights let you run it on your own hardware; Fugu Ultra v2.0 is API-only.

Anyone whose priority is sakana's own evaluation reports best-or-joint-best results on 5 of 8 benchmarks tested, including a 74.3 on the deepswe coding-repair benchmark

Fugu Ultra v2.0

It is specifically built for that.

Anyone whose priority is open-weight (apache 2.0), self-hostable

Mistral Large 3

That is its strongest area.

Fugu Ultra v2.0: where it fits

Sakana AI's flagship multi-agent orchestration model, claiming best-or-joint-best results on 5 of 8 self-reported benchmarks against monolithic frontier models — $5/$30 per million tokens. Released September 10, 2026 by Sakana AI, it is built for sakana's own evaluation reports best-or-joint-best results on 5 of 8 benchmarks tested, including a 74.3 on the DeepSWE coding-repair benchmark, claims to outperform flagship models like GPT-6 Astra and Claude Fable 5.1 despite its underlying orchestration pool reportedly excluding both of them, and 1M-token context window.

Its trade-offs are real: not a single trained model — Sakana describes Fugu as "a Multi-Agent System, Delivered as One Model" that dynamically routes tasks across a pool of other models, so head-to-head benchmark framing against monolithic frontier models should be read skeptically, pricing rises to roughly $10/$45 per million tokens for prompts above 272K tokens, and benchmark comparisons against GPT-6 Astra and Claude Fable 5.1 are Sakana's own self-reported figures, not independently verified head-to-head scores. At $5 in / $30 out per million tokens, it sits in the premium price band.

Mistral Large 3: where it fits

France's frontier contender — strong multilingual model with European data residency. Released December 2, 2025 by Mistral, it is built for open-weight (Apache 2.0), self-hostable, strong multilingual performance, efficient inference, and function calling.

Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $0.5 in / $1.5 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 3 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Fugu Ultra v2.0 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 Fugu Ultra v2.0 and Mistral Large 3 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 pricing

Frequently asked questions

Is Fugu Ultra v2.0 or Mistral Large 3 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, Fugu Ultra v2.0 leans toward sakana's own evaluation reports best-or-joint-best results on 5 of 8 benchmarks tested, including a 74.3 on the deepswe coding-repair benchmark while Mistral Large 3 leans toward open-weight (apache 2.0), self-hostable, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Fugu Ultra v2.0 or Mistral Large 3?

Mistral Large 3 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Fugu Ultra v2.0 is API-metered at $5/$30 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?

Fugu Ultra v2.0 — 1M tokens vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Fugu Ultra v2.0 and Mistral Large 3 together?

Yes — a multi-model platform like LumiChats gives you Fugu Ultra v2.0, Mistral Large 3 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, Fugu Ultra v2.0 or Mistral Large 3?

Fugu Ultra v2.0 — released September 10, 2026, about 9 months after Mistral Large 3.

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.