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 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). On a tight budget at scale, Mercury 2.5 Preview is the value pick.
Fugu Ultra v2.0 (Sakana AI) and Mercury 2.5 Preview (Inception Labs) 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. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Mercury 2.5 Preview is about 125× cheaper on input ($0.04/$0.15 per 1M tokens vs $5/$30 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Fugu Ultra v2.0 holds 3.8× more — 1M tokens (~1,500 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.
Specifications
Spec
Fugu Ultra v2.0
Mercury 2.5 Preview
Provider
Sakana AI (Global)
Inception Labs (US)
Released
September 10, 2026
August 31, 2026
Context window
1M tokens (~1,500 pages)
260K tokens (~390 pages)
Price (in/out)
$5/$30 per 1M tokens
$0.04/$0.15 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not 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 — 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.
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 — Mercury 2.5 Preview is comparatively weak here — 260K context window is far shorter than frontier 1M-token models
1M-token context window: Fugu Ultra v2.0 — Its 1M tokens window holds about 3.8× more than Mercury 2.5 Preview's 260K tokens in a single prompt.
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — Fugu Ultra v2.0 is comparatively weak here — pricing rises to roughly $10/$45 per million tokens for prompts above 272K 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 Fugu Ultra v2.0 ($5/$30 per 1M tokens), and that gap compounds at volume.
Mathematics accuracy (97.0%, 97th percentile): 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.
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: Fugu Ultra v2.0 — Its 1M tokens window is about 3.8× larger than Mercury 2.5 Preview's 260K tokens, fitting roughly 1,500 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 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.
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 very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — 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.
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: 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.
The bottom line for this matchup
Fugu Ultra v2.0 and Mercury 2.5 Preview overlap enough that the right pick depends on your specific job. Mercury 2.5 Preview costs less per token; Fugu Ultra v2.0 holds the larger context; and each leads in its own area — 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, Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Fugu Ultra v2.0 or Mercury 2.5 Preview 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 Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Fugu Ultra v2.0 or Mercury 2.5 Preview?
Mercury 2.5 Preview is cheaper — $5/$30 per 1M tokens vs $0.04/$0.15 per 1M tokens, roughly 125× apart on input.
Which has the bigger context window?
Fugu Ultra v2.0 — 1M tokens vs 260K tokens, about 3.8× 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 Mercury 2.5 Preview together?
Yes — a multi-model platform like LumiChats gives you Fugu Ultra v2.0, Mercury 2.5 Preview 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 Mercury 2.5 Preview?
Fugu Ultra v2.0 — released September 10, 2026, about 10 days after Mercury 2.5 Preview.
Fugu Ultra v2.0 vs Mercury 2.5 Preview
Sakana AI · Global | Inception Labs · US · 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 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). On a tight budget at scale, Mercury 2.5 Preview is the value pick.
Fugu Ultra v2.0 (Sakana AI) and Mercury 2.5 Preview (Inception Labs) 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. 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. 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 125× cheaper on input ($0.04/$0.15 per 1M tokens vs $5/$30 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Fugu Ultra v2.0 holds 3.8× more — 1M tokens (~1,500 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.
Side-by-side specs
Spec
Fugu Ultra v2.0
Mercury 2.5 Preview
Provider
Sakana AI (Global)
Inception Labs (US)
Released
September 10, 2026
August 31, 2026
Context window
1M tokens (~1,500 pages)
260K tokens (~390 pages)
Price (in/out)
$5/$30 per 1M tokens
$0.04/$0.15 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not 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
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.
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
Mercury 2.5 Preview is comparatively weak here — 260K context window is far shorter than frontier 1M-token models
1M-token context window
Fugu Ultra v2.0
Its 1M tokens window holds about 3.8× more than Mercury 2.5 Preview's 260K tokens in a single prompt.
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
Mercury 2.5 Preview
Fugu Ultra v2.0 is comparatively weak here — pricing rises to roughly $10/$45 per million tokens for prompts above 272K 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 Fugu Ultra v2.0 ($5/$30 per 1M tokens), and that gap compounds at volume.
Mathematics accuracy (97.0%, 97th percentile)
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.
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
Fugu Ultra v2.0
Its 1M tokens window is about 3.8× larger than Mercury 2.5 Preview's 260K tokens, fitting roughly 1,500 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 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.
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 very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
→ Mercury 2.5 Preview
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.
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: 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.
The bottom line for this matchup
Fugu Ultra v2.0 and Mercury 2.5 Preview overlap enough that the right pick depends on your specific job. Mercury 2.5 Preview costs less per token; Fugu Ultra v2.0 holds the larger context; and each leads in its own area — 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, Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Fugu Ultra v2.0 and Mercury 2.5 Preview 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.
Is Fugu Ultra v2.0 or Mercury 2.5 Preview 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 Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Fugu Ultra v2.0 or Mercury 2.5 Preview?
Mercury 2.5 Preview is cheaper — $5/$30 per 1M tokens vs $0.04/$0.15 per 1M tokens, roughly 125× apart on input.
Which has the bigger context window?
Fugu Ultra v2.0 — 1M tokens vs 260K tokens, about 3.8× 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 Mercury 2.5 Preview together?
Yes — a multi-model platform like LumiChats gives you Fugu Ultra v2.0, Mercury 2.5 Preview 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 Mercury 2.5 Preview?
Fugu Ultra v2.0 — released September 10, 2026, about 10 days after Mercury 2.5 Preview.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.