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Prompts that approach the documented context limit
Claude Fable 5
Claude Fable 5 has the larger documented context window.
Confidence: documented
Claude Fable 5 vs LFM2.5-24B-A2B
Updated October 13, 2026. Rank says Claude Fable 5 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
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Decision reading
Claude Fable 5 has the higher public point estimate, 80.9 versus 37.8. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Claude Fable 5 only
- 22
- LFM2.5-24B-A2B only
- 12
- Like-for-like categories
- 0 / 8
Supported: Claude Fable 5 · Estimated: LFM2.5-24B-A2B. Conditional ranges do not establish rank confidence.How the comparison works
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.
Chat turn cost
1K fresh input + 500 output tokens
LFM2.5-24B-A2B
LFM2.5-24B-A2B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
LFM2.5-24B-A2B
LFM2.5-24B-A2B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LFM2.5-24B-A2B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LFM2.5-24B-A2B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-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. LFM2.5-24B-A2B does not fit this workload in one request.
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.
71.4Claude Fable 528.5LFM2.5-24B-A2B
Directional only · BenchAlign v5.8
Claude Fable 5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
22 Claude Fable 5 only
1 Shared
12 LFM2.5-24B-A2B only
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-bench VerifiedCoding
Normalized gap 44.0Claude Fable 5:95%LFM2.5-24B-A2B:51%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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- Claude Fable 5
- 74.8
- Supported · #4/124
- LFM2.5-24B-A2B
- 30.2
- Estimated · #88/124
- Basis
- BenchAlign v5.8 lane · 5 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- Claude Fable 5
- 71.4
- Supported · #7/149
- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Basis
- BenchAlign v5.8 lane · 10 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Fable 5
- 82.0
- Supported · #6/181
- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- Claude Fable 5
- 77.1
- #59/128
- LFM2.5-24B-A2B
- 75.6
- #61/128
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Fable 5
- 84.3
- Unranked · 4 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5
- 66.0
- Unranked · 2 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5
- 96.0
- Unranked · 2 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5 | LFM2.5-24B-A2B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.8Supported · #4/124 | 30.2Estimated · #88/124 | Directional onlyBenchAlign v5.8 lane · 5 vs 5 public rows | Directional only |
| Coding | 71.4Supported · #7/149 | 28.5Estimated · #98/149 | Directional onlyBenchAlign v5.8 lane · 10 vs 2 public rows | Directional only |
| Knowledge | 82.0Supported · #6/181 | 40.8Estimated · #112/181 | Directional onlyBenchAlign v5.8 lane · 2 vs 3 public rows | Directional only |
| Instruction following | 77.1#59/128 | 75.6#61/128 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 84.3Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 66.0Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.0Unranked · 2 rankable rows | 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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Claude Fable 5
- $0.035
- Fits in one request
- LFM2.5-24B-A2B
- $0.0006
- Fits in one request
LFM2.5-24B-A2B has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5
- $0.65
- Fits in one request
- LFM2.5-24B-A2B
- $0.008
- Fits in one request
LFM2.5-24B-A2B has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Fable 5
- $0.9
- Fits in one request
- LFM2.5-24B-A2B
- $0.022
- Does not fit in one request
LFM2.5-24B-A2B does not fit this workload in one request.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Fable 5LFM2.5-24B-A2B
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5
LFM2.5-24B-A2B
API model ID
Claude Fable 5
claude-fable-5
Anthropic model overviewLFM2.5-24B-A2B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5
$1 per 1M cached input tokens
Claude API pricingLFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postDocumented inputs
Claude Fable 5
text, image
Anthropic model overviewLFM2.5-24B-A2B
Not sourced
Documented outputs
Claude Fable 5
LFM2.5-24B-A2B
Not sourced
Provider availability
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewLFM2.5-24B-A2B
Not sourced
Reasoning profile
Claude Fable 5
Reasoning
LFM2.5-24B-A2B
Reasoning
Weight access
Claude Fable 5
Proprietary
LFM2.5-24B-A2B
Open Weight
License
Claude Fable 5
Proprietary
LFM2.5-24B-A2B
Open Weight
Release date
Claude Fable 5
2026-06-09
LFM2.5-24B-A2B
2026-10-13
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
- Claude Fable 5 has the higher public point estimate, 80.9 versus 37.8. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.65 vs $0.008. Cache-heavy agent loop: $0.9 vs $0.022.
- Context tradeoff
- Claude Fable 5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Fable 5 or LFM2.5-24B-A2B?
Claude Fable 5 has the higher public point estimate, 80.9 versus 37.8. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Fable 5 or LFM2.5-24B-A2B?
Claude Fable 5 scores higher for coding on the public lane, 71.4 to 28.5. LFM2.5-24B-A2B 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, Claude Fable 5 or LFM2.5-24B-A2B?
Claude Fable 5 scores higher for agentic tasks on the public lane, 74.8 to 30.2. LFM2.5-24B-A2B 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, Claude Fable 5 or LFM2.5-24B-A2B?
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.65 and $0.008; the cache-heavy agent loop costs $0.9 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request.
Which has the larger context window, Claude Fable 5 or LFM2.5-24B-A2B?
Claude Fable 5 has the larger documented context window: 1M, compared with 131K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence35 rows
Agentic
Terminal-Bench 3.0
Claude Fable 534.0%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.1
Claude Fable 584.3%SourceLFM2.5-24B-A2B—Not directly comparable
OSWorld-Verified
Claude Fable 585%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 580.5%SourceLFM2.5-24B-A2B—Not directly comparable
ApprenticeBench
Claude Fable 534%SourceLFM2.5-24B-A2B—Not directly comparable
AutomationBench
Claude Fable 5—LFM2.5-24B-A2B27.7%SourceNot directly comparable
BFCL v4
Claude Fable 5—LFM2.5-24B-A2B65.0%SourceNot directly comparable
τ³-bench results
Claude Fable 5—LFM2.5-24B-A2B8.0%SourceNot directly comparable
PinchBench
Claude Fable 5—LFM2.5-24B-A2B83.2%SourceNot directly comparable
WebArena-Verified
Claude Fable 5—LFM2.5-24B-A2B26.8%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Fable 595%SourceLFM2.5-24B-A2B51%SourceClaude Fable 5 leads this result
SWE-bench Pro
Claude Fable 580%SourceLFM2.5-24B-A2B—Not directly comparable
FrontierSWE v2
Claude Fable 547.0%SourceLFM2.5-24B-A2B—Not directly comparable
FrontierCode 1.1 Main
Claude Fable 553.5%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.1
Claude Fable 584.3%SourceLFM2.5-24B-A2B—Not directly comparable
cursorBench31
Claude Fable 570.6%SourceLFM2.5-24B-A2B—Not directly comparable
CursorBench 3.2
Claude Fable 570.5%SourceLFM2.5-24B-A2B—Not directly comparable
VulcanBench v3
Claude Fable 589.5%SourceLFM2.5-24B-A2B—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 589.8%SourceLFM2.5-24B-A2B—Not directly comparable
SWE-bench (Vals)
Claude Fable 595.0%SourceLFM2.5-24B-A2B—Not directly comparable
SciCode
Claude Fable 5—LFM2.5-24B-A2B32.7%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA Diamond (Vals)
Claude Fable 593.2%SourceLFM2.5-24B-A2B—Not directly comparable
MMLU-Pro (Vals)
Claude Fable 591.5%SourceLFM2.5-24B-A2B—Not directly comparable
GPQA
Claude Fable 5—LFM2.5-24B-A2B74.4%SourceNot directly comparable
GPQA-D
Claude Fable 5—LFM2.5-24B-A2B74.4%SourceNot directly comparable
HLE
Claude Fable 5—LFM2.5-24B-A2B7.4%SourceNot directly comparable
Instruction following
Math
35 public results · 1 shared
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Last updated October 13, 2026