Long documents
Prompts that approach the documented context limit
Step 3.5 Flash
Step 3.5 Flash has the larger documented context window.
Confidence: documented
LFM2.5-24B-A2B vs Step 3.5 Flash
Updated October 13, 2026. Rank says Step 3.5 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
Share or export
Decision reading
Step 3.5 Flash has the higher public point estimate, 41.96 versus 37.8. Their conditional score ranges 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
- LFM2.5-24B-A2B only
- 12
- Step 3.5 Flash only
- 2
- Like-for-like categories
- 0 / 8
Estimated: LFM2.5-24B-A2B and Step 3.5 Flash. 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
Step 3.5 Flash
Step 3.5 Flash 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
Step 3.5 Flash
Step 3.5 Flash 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
Step 3.5 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Step 3.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
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. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
28.5LFM2.5-24B-A2B—Step 3.5 Flash
Not comparable · BenchAlign v5.8
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.
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.
12 LFM2.5-24B-A2B only
1 Shared
2 Step 3.5 Flash only
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 23.4LFM2.5-24B-A2B:51%Step 3.5 Flash: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
Not comparable- LFM2.5-24B-A2B
- 30.2
- Estimated · #88/124
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- LFM2.5-24B-A2B
- 75.6
- #61/128
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | LFM2.5-24B-A2B | Step 3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 30.2Estimated · #88/124 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 0 public rows | Not comparable |
| Coding | 28.5Estimated · #98/149 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 40.8Estimated · #112/181 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 75.6#61/128 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 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.
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
- LFM2.5-24B-A2B
- $0.0006
- Fits in one request
- Step 3.5 Flash
- $0.00025
- Fits in one request
Step 3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- LFM2.5-24B-A2B
- $0.008
- Fits in one request
- Step 3.5 Flash
- $0.0059
- Fits in one request
Step 3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- LFM2.5-24B-A2B
- $0.022
- Does not fit in one request
- Step 3.5 Flash
- $0.025
- Fits in one request
- Cached input priced at the published list-input rate
LFM2.5-24B-A2B does not fit this workload in one request. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
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.
SpecificationLFM2.5-24B-A2BStep 3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
LFM2.5-24B-A2B
Step 3.5 Flash
256K
API model ID
LFM2.5-24B-A2B
Not sourced
Step 3.5 Flash
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postStep 3.5 Flash
Not published
Documented inputs
LFM2.5-24B-A2B
Not sourced
Step 3.5 Flash
Not sourced
Documented outputs
LFM2.5-24B-A2B
Not sourced
Step 3.5 Flash
Not sourced
Provider availability
LFM2.5-24B-A2B
Not sourced
Step 3.5 Flash
Not sourced
Reasoning profile
LFM2.5-24B-A2B
Reasoning
Step 3.5 Flash
Non-Reasoning
Weight access
LFM2.5-24B-A2B
Open Weight
Step 3.5 Flash
Open Weight
License
LFM2.5-24B-A2B
Open Weight
Step 3.5 Flash
Open Weight
Release date
LFM2.5-24B-A2B
2026-10-13
Step 3.5 Flash
2026-01-20
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
- Step 3.5 Flash has the higher public point estimate, 41.96 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.008 vs $0.0059. Cache-heavy agent loop: $0.022 vs $0.025.
- Context tradeoff
- Step 3.5 Flash has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, LFM2.5-24B-A2B or Step 3.5 Flash?
Step 3.5 Flash has the higher public point estimate, 41.96 versus 37.8. Their conditional score ranges 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, LFM2.5-24B-A2B or Step 3.5 Flash?
Step 3.5 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, LFM2.5-24B-A2B or Step 3.5 Flash?
Step 3.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, LFM2.5-24B-A2B or Step 3.5 Flash?
For the stated presets, chat costs $0.0006 on LFM2.5-24B-A2B and $0.00025 on Step 3.5 Flash; repository review costs $0.008 and $0.0059; the cache-heavy agent loop costs $0.022 and $0.025. LFM2.5-24B-A2B does not fit this workload in one request. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, LFM2.5-24B-A2B or Step 3.5 Flash?
Step 3.5 Flash has the larger documented context window: 256K, 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 evidence15 rows
Agentic
AutomationBench
LFM2.5-24B-A2B27.7%SourceStep 3.5 Flash—Not directly comparable
BFCL v4
LFM2.5-24B-A2B65.0%SourceStep 3.5 Flash—Not directly comparable
τ³-bench results
LFM2.5-24B-A2B8.0%SourceStep 3.5 Flash—Not directly comparable
PinchBench
LFM2.5-24B-A2B83.2%SourceStep 3.5 Flash—Not directly comparable
WebArena-Verified
LFM2.5-24B-A2B26.8%SourceStep 3.5 Flash—Not directly comparable
Coding
Knowledge
Instruction following
Math
15 public results · 1 shared
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Last updated October 13, 2026