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 GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Choose GLM 5.1 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 GLM 5.1 (Z.ai) 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. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GLM 5.1 is about 3.6× cheaper on input ($1.4/$4.4 per 1M tokens vs $5/$30 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Fugu Ultra v2.0 holds 5× more — 1M tokens (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Fugu Ultra v2.0 is the newer model by about 5 months (released September 10, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Fugu Ultra v2.0
GLM 5.1
Provider
Sakana AI (Global)
Z.ai (China)
Released
September 10, 2026
April 7, 2026
Context window
1M tokens (~1,500 pages)
200K (~300 pages)
Price (in/out)
$5/$30 per 1M tokens
$1.4/$4.4 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text
text, code
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 — 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 is the newer of the two.
1M-token context window: Fugu Ultra v2.0 — Its 1M tokens window holds about 5× more than GLM 5.1's 200K in a single prompt.
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it runs cheaper at $1.4/$4.4 per 1M tokens.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — Open weights make this possible at all — Fugu Ultra v2.0 is API-only, so it cannot leave the vendor's servers.
Sustained tool use across thousands of calls: GLM 5.1 — Fugu Ultra v2.0 is comparatively weak here — 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
Lowest cost at scale: GLM 5.1 — At $1.4/$4.4 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 5× larger than GLM 5.1's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 5.1 — At $1.4/$4.4 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: GLM 5.1 — 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 long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5.1 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.
Frequently asked questions
Is Fugu Ultra v2.0 or GLM 5.1 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, 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 GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Fugu Ultra v2.0 or GLM 5.1?
GLM 5.1 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 200K, about 5× 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 GLM 5.1 together?
Yes — a multi-model platform like LumiChats gives you Fugu Ultra v2.0, GLM 5.1 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, Fugu Ultra v2.0 or GLM 5.1?
Fugu Ultra v2.0 — released September 10, 2026, about 5 months after GLM 5.1.
Fugu Ultra v2.0 vs GLM 5.1
Sakana AI · Global | Z.ai · China · 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 GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Choose GLM 5.1 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 GLM 5.1 (Z.ai) 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. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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: GLM 5.1 is about 3.6× cheaper on input ($1.4/$4.4 per 1M tokens vs $5/$30 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Fugu Ultra v2.0 holds 5× more — 1M tokens (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Fugu Ultra v2.0 is the newer model by about 5 months (released September 10, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Fugu Ultra v2.0
GLM 5.1
Provider
Sakana AI (Global)
Z.ai (China)
Released
September 10, 2026
April 7, 2026
Context window
1M tokens (~1,500 pages)
200K (~300 pages)
Price (in/out)
$5/$30 per 1M tokens
$1.4/$4.4 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text
text, code
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
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 is the newer of the two.
1M-token context window
Fugu Ultra v2.0
Its 1M tokens window holds about 5× more than GLM 5.1's 200K in a single prompt.
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it runs cheaper at $1.4/$4.4 per 1M tokens.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
Open weights make this possible at all — Fugu Ultra v2.0 is API-only, so it cannot leave the vendor's servers.
Sustained tool use across thousands of calls
GLM 5.1
Fugu Ultra v2.0 is comparatively weak here — 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
Lowest cost at scale
GLM 5.1
At $1.4/$4.4 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 5× larger than GLM 5.1's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 5.1
At $1.4/$4.4 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
→ GLM 5.1
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 long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5.1 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 GLM 5.1 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.
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 GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Fugu Ultra v2.0 or GLM 5.1?
GLM 5.1 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 200K, about 5× 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 GLM 5.1 together?
Yes — a multi-model platform like LumiChats gives you Fugu Ultra v2.0, GLM 5.1 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, Fugu Ultra v2.0 or GLM 5.1?
Fugu Ultra v2.0 — released September 10, 2026, about 5 months after GLM 5.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.