AI Models · Paid vs Paid

SAFugu Ultra v2 Paid vs Mistral Medium 3.1 (batch) Paid

Fugu Ultra v2 vs Mistral Medium 3.1 (batch) compared — price per token, context window, multimodality, openness and which to choose. Full 2026 breakdown.

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

SAFugu Ultra v2PaidSakana AI
$5 /M input
$30 /M outputManaged API (no infra to run)
TypeProprietary
Context window1M tokens
MultimodalYes
Self-hostNo
Mistral Medium 3.1 (batch)PaidMistral AI
$0.2 /M input
$1 /M outputManaged API (no infra to run)
TypeProprietary
Context window131K tokens
MultimodalYes
Self-hostNo
Choose Mistral Medium 3.1 (batch) for the lower output price ($1/M vs $30/M). Choose Fugu Ultra v2 if you need the larger 1M context window.

Fugu Ultra v2 vs Mistral Medium 3.1 (batch) specs

SpecFugu Ultra v2Mistral Medium 3.1 (batch)Winner
MakerSakana AIMistral AI
TypeProprietaryProprietary
Context window1M tokens131K tokensSAFugu Ultra v2
Input price$5/M$0.2/MMistral Medium 3.1 (batch)
Output price$30/M$1/MMistral Medium 3.1 (batch)
Vision / multimodalYesYes= Tie
Tool / function callingYesYes= Tie
Self-hostableNo (API only)No (API only)
LicenseProprietaryProprietary

Price gap & when to choose each

30×cheaper per output token

Mistral Medium 3.1 (batch) is ~30× cheaper than Fugu Ultra v2 on output tokens ($1 vs $30 per M tokens).

SAChoose Fugu Ultra v2 if…
  • You want frontier performance through a managed API
  • You need the longer 1M context window
  • You value reliability & ecosystem
  • You don't want to manage infrastructure
Choose Mistral Medium 3.1 (batch) if…
  • You want frontier performance through a managed API
  • You want the lower output price
  • You value reliability & ecosystem
  • You don't want to manage infrastructure

Feature comparison

CapabilityFugu Ultra v2Mistral Medium 3.1 (batch)
Open weights (downloadable)
Self-hostable
Runs fully offline
Vision / multimodal
Tool / function calling
1M+ context window

Benchmarks: Fugu Ultra v2 vs Mistral Medium 3.1 (batch)

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

Fugu Ultra v2
Mistral Medium 3.1 (batch)
Intelligence index
9.9
Coding index
20.5
Math index
38.3
GPQA
58.8%
MMLU-Pro
68.3%
Humanity's Last Exam
4.7%
LiveCodeBench
40.6%
SciCode
32.4%
AIME 2025
38.3%
IFBench
39.8%
τ²-Bench
40.6%
τ-Bench Banking
8.2%
Terminal-Bench
13.9%
Terminal-Bench Hard
10.6%
Speed
0 tok/s
Latency
0s
Intelligence per $
12.4

Benchmark data by Artificial Analysis.

How Fugu Ultra v2 and Mistral Medium 3.1 (batch) score

🤝 Neck and neck on these criteria (3.1 vs 3.1 / 5).
CriterionFugu Ultra v2Mistral Medium 3.1 (batch)
Cost-efficiency3.04.5
Context window5.03.5
Openness1.51.5
Self-hosting1.01.0
Multimodality5.05.0

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

Fugu Ultra v2 Paid

Sakana AI · Proprietary

Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to...

Mistral Medium 3.1 (batch) Paid

Mistral AI · Proprietary

Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances...

Frequently asked questions

Fugu Ultra v2 vs Mistral Medium 3.1 (batch) — which is cheaper?

Mistral Medium 3.1 (batch) is cheaper on output ($1/M vs $30/M).

Which has the larger context window?

Fugu Ultra v2 offers the larger context window (1M tokens).

Fugu Ultra v2 vs Mistral Medium 3.1 (batch) — which should I pick in 2026?

Choose Mistral Medium 3.1 (batch) for the lower output price ($1/M vs $30/M). Choose Fugu Ultra v2 if you need the larger 1M context window.

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