Agentic
Not comparable- LFM2.5-2.6B
- 37.2
- Estimated · #131/154
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Not comparable
LFM2.5-2.6B vs Sakana Fugu-Ultra
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
Sakana Fugu-Ultra
Sakana Fugu-Ultra has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Sakana Fugu-Ultra is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Sakana Fugu-Ultra is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. Sakana Fugu-Ultra has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
39.2LFM2.5-2.6B—Sakana Fugu-Ultra
Not comparable · BenchAlign
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 LFM2.5-2.6B only
1 Shared
10 Sakana Fugu-Ultra only
- Shared results
- 1
- LFM2.5-2.6B only
- 6
- Sakana Fugu-Ultra only
- 10
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- LFM2.5-2.6B
- 39.2
- Estimated · #125/154
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 1 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- LFM2.5-2.6B
- 24.9
- Unranked · 2 rankable rows
- Sakana Fugu-Ultra
- 77.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- LFM2.5-2.6B
- 37.0
- Estimated · #151/184
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- LFM2.5-2.6B
- Not ranked
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-2.6B
- Not ranked
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-2.6B
- Not ranked
- Sakana Fugu-Ultra
- 72.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- LFM2.5-2.6B
- 38.1
- #104/124
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | LFM2.5-2.6B | Sakana Fugu-Ultra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 37.2Estimated · #131/154 | Not ranked | Not comparableBenchAlign lane · 4 vs 1 public rows | Not comparable |
| Coding | 39.2Estimated · #125/154 | Not ranked | Not comparableBenchAlign lane · 1 vs 5 public rows | Not comparable |
| Reasoning | 24.9Unranked · 2 rankable rows | 77.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 37.0Estimated · #151/184 | Not ranked | Not comparableBenchAlign lane · 0 vs 3 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 72.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | 38.1#104/124 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate. Sakana Fugu-Ultra has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate. Sakana Fugu-Ultra has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
- Cached-input rate unavailable
LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. Sakana Fugu-Ultra has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationLFM2.5-2.6BSakana Fugu-Ultra
Context window
Maximum documented context; output-token limits may be lower.
LFM2.5-2.6B
Sakana Fugu-Ultra
1M
API model ID
LFM2.5-2.6B
Not sourced
Sakana Fugu-Ultra
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LFM2.5-2.6B
No comparable hosted API rate
LiquidAI Hugging Face model cardSakana Fugu-Ultra
No comparable hosted API rate
Documented inputs
LFM2.5-2.6B
Not sourced
Sakana Fugu-Ultra
Not sourced
Documented outputs
LFM2.5-2.6B
Not sourced
Sakana Fugu-Ultra
Not sourced
Provider availability
LFM2.5-2.6B
Not sourced
Sakana Fugu-Ultra
Not sourced
Reasoning profile
LFM2.5-2.6B
Reasoning
Sakana Fugu-Ultra
Reasoning
Weight access
LFM2.5-2.6B
Open Weight
Sakana Fugu-Ultra
Proprietary
License
LFM2.5-2.6B
Open Weight
Sakana Fugu-Ultra
Proprietary
Release date
LFM2.5-2.6B
2026-08-04
Sakana Fugu-Ultra
2026-06-22
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Sakana Fugu-Ultra has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence17 rows
Agentic
BFCL v4
LFM2.5-2.6B56.9%SourceSakana Fugu-Ultra—Not directly comparable
τ³-bench results
LFM2.5-2.6B5.7%SourceSakana Fugu-Ultra—Not directly comparable
Claw-Eval
LFM2.5-2.6B62.9%SourceSakana Fugu-Ultra—Not directly comparable
PinchBench
LFM2.5-2.6B68.2%SourceSakana Fugu-Ultra—Not directly comparable
Terminal-Bench 2.0
LFM2.5-2.6B—Sakana Fugu-Ultra82.1%SourceNot directly comparable
Coding
LiveCodeBench v6
LFM2.5-2.6B59.4%SourceSakana Fugu-Ultra93.2%SourceSakana Fugu-Ultra leads this result
SWE-bench Pro
LFM2.5-2.6B—Sakana Fugu-Ultra73.7%SourceNot directly comparable
Terminal-Bench 2.0
LFM2.5-2.6B—Sakana Fugu-Ultra82.1%SourceNot directly comparable
LiveCodeBench Pro
LFM2.5-2.6B—Sakana Fugu-Ultra90.8%SourceNot directly comparable
SciCode
LFM2.5-2.6B—Sakana Fugu-Ultra58.7%SourceNot directly comparable
Reasoning
MRCRv2
LFM2.5-2.6B—Sakana Fugu-Ultra93.6%SourceNot directly comparable
Knowledge
Math
AIME 2025
LFM2.5-2.6B51.9%SourceSakana Fugu-Ultra—Not directly comparable
Multimodal
CharXiv
LFM2.5-2.6B—Sakana Fugu-Ultra86.6%SourceNot directly comparable
Instruction following
IFBench
LFM2.5-2.6B59.2%SourceSakana Fugu-Ultra—Not directly comparable
Questions
Which is better, LFM2.5-2.6B or Sakana Fugu-Ultra?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, LFM2.5-2.6B or Sakana Fugu-Ultra?
Sakana Fugu-Ultra is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, LFM2.5-2.6B or Sakana Fugu-Ultra?
Sakana Fugu-Ultra is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, LFM2.5-2.6B or Sakana Fugu-Ultra?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, LFM2.5-2.6B or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 18, 2026
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