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BenchLM
Data

Claude Haiku 5.5 vs Step 3.7 Flash

Updated October 7, 2026. Rank says Claude Haiku 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Haiku 5.5 has the higher public point estimate, 66.32 versus 44.21. 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.

Model A
Anthropic logo

Anthropic

66.32/100

Estimated · Public rank #28

Conditional range 52.0–80.7

Model B
StepFun logo

StepFun

44.21/100

Estimated · Public rank #114

Conditional range 29.9–58.6

Shared results
1
Claude Haiku 5.5 only
4
Step 3.7 Flash only
10
Like-for-like categories
0 / 8
Estimated: Claude Haiku 5.5 and Step 3.7 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.

  • Long documents

    Prompts that approach the documented context limit

    Claude Haiku 5.5

    Claude Haiku 5.5 has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Haiku 5.5

    Claude Haiku 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Claude Haiku 5.5

    Claude Haiku 5.5 has the lower estimated token cost for this stated workload. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Haiku 5.5

    Claude Haiku 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Step 3.7 Flash 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

    Step 3.7 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

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.

60.9Claude Haiku 5.532.2Step 3.7 Flash

Directional only · BenchAlign v5.8

Claude Haiku 5.5 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.

2 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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 Haiku 5.5
62.1
Supported · #18/122
Step 3.7 Flash
36.0
Estimated · #71/122
Basis
BenchAlign v5.8 lane · 2 vs 7 public rows
Reading
Directional only

Coding

Directional only
Claude Haiku 5.5
60.9
Supported · #20/146
Step 3.7 Flash
32.2
Estimated · #90/146
Basis
BenchAlign v5.8 lane · 1 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Haiku 5.5
79.2
Unranked · 2 rankable rows
Step 3.7 Flash
73.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Haiku 5.5
Not ranked
Step 3.7 Flash
72.0
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Haiku 5.5
Not ranked
Step 3.7 Flash
43.9
Estimated · #89/174
Basis
BenchAlign v5.8 lane · 1 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude Haiku 5.5
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Haiku 5.5
Not ranked
Step 3.7 Flash
80.6
#53/125
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Haiku 5.5
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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 Haiku 5.5
$0.00035
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

Claude Haiku 5.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Haiku 5.5
$0.0065
Fits in one request
Step 3.7 Flash
$0.01345
Fits in one request

Claude Haiku 5.5 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 Haiku 5.5
$0.009
Fits in one request
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

Claude Haiku 5.5 has the lower modeled cost

Step 3.7 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.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Haiku 5.5

$0.01 per 1M cached input tokens

Claude API pricing

Step 3.7 Flash

Not published

Reasoning profile

Claude Haiku 5.5

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Claude Haiku 5.5

Proprietary

Step 3.7 Flash

Open Weight

License

Claude Haiku 5.5

Proprietary

Step 3.7 Flash

Open Weight

Release date

Claude Haiku 5.5

2026-10-07

Step 3.7 Flash

2026-05-29

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 Haiku 5.5 has the higher public point estimate, 66.32 versus 44.21. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.0065 vs $0.01345. Cache-heavy agent loop: $0.009 vs $0.0555.
Context tradeoff
Claude Haiku 5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Haiku 5.5 or Step 3.7 Flash?

Claude Haiku 5.5 has the higher public point estimate, 66.32 versus 44.21. 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, Claude Haiku 5.5 or Step 3.7 Flash?

Claude Haiku 5.5 scores higher for coding on the public lane, 60.9 to 32.2. Step 3.7 Flash 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 Haiku 5.5 or Step 3.7 Flash?

Claude Haiku 5.5 scores higher for agentic tasks on the public lane, 62.1 to 36. Step 3.7 Flash 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 Haiku 5.5 or Step 3.7 Flash?

For the stated presets, chat costs $0.00035 on Claude Haiku 5.5 and $0.00077 on Step 3.7 Flash; repository review costs $0.0065 and $0.01345; the cache-heavy agent loop costs $0.009 and $0.0555. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Haiku 5.5 or Step 3.7 Flash?

Claude Haiku 5.5 has the larger documented context window: 1M, compared with 256K.

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

  • Terminal-Bench 4.0

    Claude Haiku 5.539.20%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HLE w/ tools

    Claude Haiku 5.557.4%
    Source
    Step 3.7 Flash47.2%
    Source

    Claude Haiku 5.5 leads this result

  • Terminal-Bench 2.1

    Claude Haiku 5.5—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

  • BrowseComp

    Claude Haiku 5.5—
    Step 3.7 Flash75.8%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude Haiku 5.5—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Toolathlon

    Claude Haiku 5.5—
    Step 3.7 Flash49.5%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Haiku 5.5—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • Gert Labs

    Claude Haiku 5.5—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Claude Haiku 5.546.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench Pro

    Claude Haiku 5.5—
    Step 3.7 Flash56.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Haiku 5.5—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Claude Haiku 5.546.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SimpleVQA

    Claude Haiku 5.5—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    Claude Haiku 5.5—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Knowledge

  • HLE w/o tools

    Claude Haiku 5.545.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

15 public results · 1 shared

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Last updated October 7, 2026