Instruction following
Like-for-like- Inkling-Small
- 89.2
- #20/124
- LFM2.5-2.6B
- 38.1
- #104/124
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Inkling-Small leads
Inkling-Small vs LFM2.5-2.6B
Decision reading
Inkling-Small has the higher public score, 58.78 versus 39.9, and the 90% score intervals do not overlap.
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. Rank says Inkling-Small is ahead. Price, access, and your workload can each overturn that. 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
Inkling-Small
Inkling-Small 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
LFM2.5-2.6B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LFM2.5-2.6B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
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.
43.7Inkling-Small39.2LFM2.5-2.6B
Directional only · BenchAlign
Inkling-Small scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
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.
24 Inkling-Small only
1 Shared
6 LFM2.5-2.6B only
- Shared results
- 1
- Inkling-Small only
- 24
- LFM2.5-2.6B only
- 6
- Like-for-like categories
- 1 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Directional only- Inkling-Small
- 36.6
- Supported · #134/154
- LFM2.5-2.6B
- 37.2
- Estimated · #131/154
- Basis
- BenchAlign lane · 5 vs 4 public rows
- Reading
- Directional only
Coding
Directional only- Inkling-Small
- 43.7
- Supported · #101/154
- LFM2.5-2.6B
- 39.2
- Estimated · #125/154
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Inkling-Small
- 56.3
- Supported · #47/184
- LFM2.5-2.6B
- 37.0
- Estimated · #151/184
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- Inkling-Small
- 42.7
- Unranked · 3 rankable rows
- LFM2.5-2.6B
- 24.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Inkling-Small
- 76.5
- Unranked · 2 rankable rows
- LFM2.5-2.6B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Inkling-Small
- Not ranked
- LFM2.5-2.6B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Inkling-Small
- 48.8
- #39/48
- LFM2.5-2.6B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Inkling-Small | LFM2.5-2.6B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 89.2#20/124 | 38.1#104/124 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Inkling-Small leads |
| Agentic | 36.6Supported · #134/154 | 37.2Estimated · #131/154 | Directional onlyBenchAlign lane · 5 vs 4 public rows | Directional only |
| Coding | 43.7Supported · #101/154 | 39.2Estimated · #125/154 | Directional onlyBenchAlign lane · 6 vs 1 public rows | Directional only |
| Knowledge | 56.3Supported · #47/184 | 37.0Estimated · #151/184 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Reasoning | 42.7Unranked · 3 rankable rows | 24.9Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 76.5Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 48.8#39/48 | Not ranked | Not comparableProvisional lane · 2 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
IFBench
Instruction following
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
- Inkling-Small
- $0.0013
- Fits in one request
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Inkling-Small
- $0.03332
- Fits in one request
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Inkling-Small
- $0.0492
- Fits in one request
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Does not fit 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationInkling-SmallLFM2.5-2.6B
Context window
Maximum documented context; output-token limits may be lower.
Inkling-Small
1M
LFM2.5-2.6B
API model ID
Inkling-Small
Not sourced
LFM2.5-2.6B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling-Small
$0.116 per 1M cached input tokens
LFM2.5-2.6B
No comparable hosted API rate
LiquidAI Hugging Face model cardDocumented inputs
Inkling-Small
Not sourced
LFM2.5-2.6B
Not sourced
Documented outputs
Inkling-Small
Not sourced
LFM2.5-2.6B
Not sourced
Provider availability
Inkling-Small
Not sourced
LFM2.5-2.6B
Not sourced
Reasoning profile
Inkling-Small
Hybrid
LFM2.5-2.6B
Reasoning
Weight access
Inkling-Small
Open Weight
LFM2.5-2.6B
Open Weight
License
Inkling-Small
Open Weight
LFM2.5-2.6B
Open Weight
Release date
Inkling-Small
2026-07-30
LFM2.5-2.6B
2026-08-04
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
- Inkling-Small has the higher public score, 58.78 versus 39.9, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Inkling-Small 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 evidence31 rows
Agentic
Terminal-Bench 2.0
Inkling-Small64.7%SourceLFM2.5-2.6B—Not directly comparable
BrowseComp
Inkling-Small77.4%SourceLFM2.5-2.6B—Not directly comparable
MCP Atlas
Inkling-Small79.6%SourceLFM2.5-2.6B—Not directly comparable
Toolathlon-Verified
Inkling-Small54.4%SourceLFM2.5-2.6B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Inkling-Small55.1%SourceLFM2.5-2.6B—Not directly comparable
BFCL v4
Inkling-Small—LFM2.5-2.6B56.9%SourceNot directly comparable
τ³-bench results
Inkling-Small—LFM2.5-2.6B5.7%SourceNot directly comparable
Claw-Eval
Inkling-Small—LFM2.5-2.6B62.9%SourceNot directly comparable
PinchBench
Inkling-Small—LFM2.5-2.6B68.2%SourceNot directly comparable
Coding
SWE-bench Verified
Inkling-Small80.2%SourceLFM2.5-2.6B—Not directly comparable
SWE-bench Pro
Inkling-Small55.9%SourceLFM2.5-2.6B—Not directly comparable
Terminal-Bench 2.0
Inkling-Small64.7%SourceLFM2.5-2.6B—Not directly comparable
SciCode
Inkling-Small48.7%SourceLFM2.5-2.6B—Not directly comparable
LiveCodeBench (Vals)
Inkling-Small85.9%SourceLFM2.5-2.6B—Not directly comparable
SWE-bench (Vals)
Inkling-Small82.2%SourceLFM2.5-2.6B—Not directly comparable
LiveCodeBench v6
Inkling-Small—LFM2.5-2.6B59.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Inkling-Small89.5%SourceLFM2.5-2.6B—Not directly comparable
GPQA-D
Inkling-Small89.5%SourceLFM2.5-2.6B—Not directly comparable
HLE
Inkling-Small47.8%SourceLFM2.5-2.6B—Not directly comparable
HLE w/o tools
Inkling-Small31.6%SourceLFM2.5-2.6B—Not directly comparable
GPQA Diamond (Vals)
Inkling-Small83.6%SourceLFM2.5-2.6B—Not directly comparable
MMLU-Pro (Vals)
Inkling-Small85.6%SourceLFM2.5-2.6B—Not directly comparable
Math
Multimodal
Instruction following
Questions
Which is better, Inkling-Small or LFM2.5-2.6B?
Inkling-Small has the higher public score, 58.78 versus 39.9, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Inkling-Small or LFM2.5-2.6B?
Inkling-Small scores higher for coding on the public lane, 43.7 to 39.2. LFM2.5-2.6B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Inkling-Small or LFM2.5-2.6B?
LFM2.5-2.6B scores higher for agentic tasks on the public lane, 37.2 to 36.6. LFM2.5-2.6B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Inkling-Small or LFM2.5-2.6B?
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, Inkling-Small or LFM2.5-2.6B?
Inkling-Small has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 18, 2026
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