Long documents
Prompts that approach the documented context limit
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the larger documented context window.
Confidence: documented
DeepSeek V4.1 Flash vs LFM2.5-24B-A2B
Updated October 13, 2026. Rank says DeepSeek V4.1 Flash 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
DeepSeek V4.1 Flash has the higher public point estimate, 64.59 versus 37.8. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- DeepSeek V4.1 Flash only
- 25
- LFM2.5-24B-A2B only
- 9
- Like-for-like categories
- 0 / 8
Supported: DeepSeek V4.1 Flash · 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.
61.0DeepSeek V4.1 Flash28.5LFM2.5-24B-A2B
Directional only · BenchAlign v5.8
DeepSeek V4.1 Flash 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.
25 DeepSeek V4.1 Flash only
4 Shared
9 LFM2.5-24B-A2B only
3 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.
HLEKnowledge
Normalized gap 29.4DeepSeek V4.1 Flash:36.8%LFM2.5-24B-A2B:7.4%AutomationBenchAgentic
Normalized gap 27.1DeepSeek V4.1 Flash:54.8%LFM2.5-24B-A2B:27.7%GPQAKnowledge
Normalized gap 16.5DeepSeek V4.1 Flash:90.9%LFM2.5-24B-A2B:74.4%
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- DeepSeek V4.1 Flash
- 60.4
- Supported · #22/124
- LFM2.5-24B-A2B
- 30.2
- Estimated · #88/124
- Basis
- BenchAlign v5.8 lane · 10 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- DeepSeek V4.1 Flash
- 61.0
- Supported · #17/149
- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Basis
- BenchAlign v5.8 lane · 8 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- DeepSeek V4.1 Flash
- 65.1
- Supported · #34/181
- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4.1 Flash
- 83.4
- Unranked · 4 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4.1 Flash
- 77.9
- Unranked · 1 rankable row
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- LFM2.5-24B-A2B
- 75.6
- #61/128
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4.1 Flash
- 70.1
- Unranked · 3 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4.1 Flash | LFM2.5-24B-A2B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.4Supported · #22/124 | 30.2Estimated · #88/124 | Directional onlyBenchAlign v5.8 lane · 10 vs 5 public rows | Directional only |
| Coding | 61.0Supported · #17/149 | 28.5Estimated · #98/149 | Directional onlyBenchAlign v5.8 lane · 8 vs 2 public rows | Directional only |
| Knowledge | 65.1Supported · #34/181 | 40.8Estimated · #112/181 | Directional onlyBenchAlign v5.8 lane · 3 vs 3 public rows | Directional only |
| Reasoning | 83.4Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 77.9Unranked · 1 rankable row | 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 |
| Instruction following | Not ranked | 75.6#61/128 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 70.1Unranked · 3 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
- DeepSeek V4.1 Flash
- $0.0009
- 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
- DeepSeek V4.1 Flash
- $0.0186
- 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
- DeepSeek V4.1 Flash
- $0.0192
- 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.
SpecificationDeepSeek V4.1 FlashLFM2.5-24B-A2B
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4.1 Flash
LFM2.5-24B-A2B
API model ID
DeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingLFM2.5-24B-A2B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingLFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postDocumented inputs
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideLFM2.5-24B-A2B
Not sourced
Documented outputs
DeepSeek V4.1 Flash
LFM2.5-24B-A2B
Not sourced
Provider availability
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseLFM2.5-24B-A2B
Not sourced
Reasoning profile
DeepSeek V4.1 Flash
Reasoning
LFM2.5-24B-A2B
Reasoning
Weight access
DeepSeek V4.1 Flash
Open Weight
LFM2.5-24B-A2B
Open Weight
License
DeepSeek V4.1 Flash
Open Weight
LFM2.5-24B-A2B
Open Weight
Release date
DeepSeek V4.1 Flash
2026-09-10
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
- DeepSeek V4.1 Flash has the higher public point estimate, 64.59 versus 37.8. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0186 vs $0.008. Cache-heavy agent loop: $0.0192 vs $0.022.
- Context tradeoff
- DeepSeek V4.1 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V4.1 Flash or LFM2.5-24B-A2B?
DeepSeek V4.1 Flash has the higher public point estimate, 64.59 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, DeepSeek V4.1 Flash or LFM2.5-24B-A2B?
DeepSeek V4.1 Flash scores higher for coding on the public lane, 61 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, DeepSeek V4.1 Flash or LFM2.5-24B-A2B?
DeepSeek V4.1 Flash scores higher for agentic tasks on the public lane, 60.4 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, DeepSeek V4.1 Flash or LFM2.5-24B-A2B?
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.0186 and $0.008; the cache-heavy agent loop costs $0.0192 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request.
Which has the larger context window, DeepSeek V4.1 Flash or LFM2.5-24B-A2B?
DeepSeek V4.1 Flash 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 evidence38 rows
Agentic
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceLFM2.5-24B-A2B—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 4.0
DeepSeek V4.1 Flash31.20%SourceLFM2.5-24B-A2B—Not directly comparable
CyberGym
DeepSeek V4.1 Flash88.1%SourceLFM2.5-24B-A2B—Not directly comparable
ExploitGym
DeepSeek V4.1 Flash15.3%SourceLFM2.5-24B-A2B—Not directly comparable
HLE w/ tools
DeepSeek V4.1 Flash63.9%SourceLFM2.5-24B-A2B—Not directly comparable
AutomationBench
DeepSeek V4.1 Flash54.8%SourceLFM2.5-24B-A2B27.7%SourceDeepSeek V4.1 Flash leads this result
Agents' Last Exam
DeepSeek V4.1 Flash31.8%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4.1 Flash74.5%SourceLFM2.5-24B-A2B—Not directly comparable
CWE-bench v1
DeepSeek V4.1 Flash55.0%SourceLFM2.5-24B-A2B—Not directly comparable
BFCL v4
DeepSeek V4.1 Flash—LFM2.5-24B-A2B65.0%SourceNot directly comparable
τ³-bench results
DeepSeek V4.1 Flash—LFM2.5-24B-A2B8.0%SourceNot directly comparable
PinchBench
DeepSeek V4.1 Flash—LFM2.5-24B-A2B83.2%SourceNot directly comparable
WebArena-Verified
DeepSeek V4.1 Flash—LFM2.5-24B-A2B26.8%SourceNot directly comparable
Coding
Codeforces
DeepSeek V4.1 Flash3471.0SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceLFM2.5-24B-A2B—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceLFM2.5-24B-A2B—Not directly comparable
DeepSWE
DeepSeek V4.1 Flash74.2%SourceLFM2.5-24B-A2B—Not directly comparable
ProgramBench
DeepSeek V4.1 Flash20.3%SourceLFM2.5-24B-A2B—Not directly comparable
NL2Repo
DeepSeek V4.1 Flash65.4%SourceLFM2.5-24B-A2B—Not directly comparable
OpenHarmony Bench
DeepSeek V4.1 Flash60.3%SourceLFM2.5-24B-A2B—Not directly comparable
Bug Hunt Bench
DeepSeek V4.1 Flash21.7 fixesSourceLFM2.5-24B-A2B—Not directly comparable
SciCode
DeepSeek V4.1 Flash—LFM2.5-24B-A2B32.7%SourceNot directly comparable
SWE-bench Verified
DeepSeek V4.1 Flash—LFM2.5-24B-A2B51%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
Instruction following
Math
Apex
DeepSeek V4.1 Flash65.6%SourceLFM2.5-24B-A2B—Not directly comparable
FrontierMath v2 (Tiers 1-3)
DeepSeek V4.1 Flash67.368%SourceLFM2.5-24B-A2B—Not directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V4.1 Flash26.829%SourceLFM2.5-24B-A2B—Not directly comparable
MATH-500
DeepSeek V4.1 Flash—LFM2.5-24B-A2B96.0%SourceNot directly comparable
38 public results · 4 shared
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Last updated October 13, 2026