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BenchLM

Claude 3 Haiku vs Claude Sonnet 4.5

Updated September 29, 2026. Rank says Claude Sonnet 4.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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

14.78/100

Supported · Public rank #202

90% interval 7.3–22.3

Model B
Anthropic logo

Anthropic

47.85/100

Estimated · Public rank #90

90% interval 42.1–53.6

Shared results
0
Claude 3 Haiku only
0
Claude Sonnet 4.5 only
11
Like-for-like categories
0 / 8
Supported: Claude 3 Haiku · Estimated: Claude Sonnet 4.5How 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

    Claude 3 Haiku

    Claude 3 Haiku 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

    Claude 3 Haiku

    Claude 3 Haiku 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

    Claude 3 Haiku and Claude Sonnet 4.5 are not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Claude 3 Haiku is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented
  • 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. Claude 3 Haiku does not fit this workload in one request. Claude Sonnet 4.5 does not fit this workload in one request. Claude 3 Haiku has no published cached-input rate, so cached tokens use its listed input rate. Claude Sonnet 4.5 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.

—Claude 3 Haiku—Claude Sonnet 4.5

Not comparable · BenchAlign v5.7

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.

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 v5.7 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
Claude 3 Haiku
Not ranked
Claude Sonnet 4.5
34.6
Estimated · #67/117
Basis
BenchAlign v5.7 lane · 0 vs 5 public rows
Reading
Not comparable

Coding

Not comparable
Claude 3 Haiku
Not ranked
Claude Sonnet 4.5
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude 3 Haiku
41.2
Unranked · 2 rankable rows
Claude Sonnet 4.5
19.2
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude 3 Haiku
26.4
Unranked · 1 rankable row
Claude Sonnet 4.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude 3 Haiku
20.1
Estimated · #167/169
Claude Sonnet 4.5
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude 3 Haiku
Not ranked
Claude Sonnet 4.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude 3 Haiku
39.9
#102/124
Claude Sonnet 4.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude 3 Haiku
Not ranked
Claude Sonnet 4.5
34.6
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

Claude 3 Haiku
$0.00088
Fits in one request
Claude Sonnet 4.5
$0.0105
Fits in one request

Claude 3 Haiku has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude 3 Haiku
$0.01625
Fits in one request
Claude Sonnet 4.5
$0.195
Fits in one request

Claude 3 Haiku 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 3 Haiku
$0.0675
Does not fit in one request
Cached input priced at the published list-input rate
Claude Sonnet 4.5
$0.81
Does not fit in one request
Cached input priced at the published list-input rate

Claude 3 Haiku does not fit this workload in one request. Claude Sonnet 4.5 does not fit this workload in one request. Claude 3 Haiku has no published cached-input rate, so cached tokens use its listed input rate. Claude Sonnet 4.5 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude 3 Haiku

200K

Claude Sonnet 4.5

200K

API model ID

Claude 3 Haiku

Not sourced

Claude Sonnet 4.5

Not sourced

Cached-input rate

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

Claude 3 Haiku

Not published

Claude Sonnet 4.5

Not published

Documented inputs

Claude 3 Haiku

Not sourced

Claude Sonnet 4.5

Not sourced

Documented outputs

Claude 3 Haiku

Not sourced

Claude Sonnet 4.5

Not sourced

Provider availability

Claude 3 Haiku

Not sourced

Claude Sonnet 4.5

Not sourced

Reasoning profile

Claude 3 Haiku

Non-Reasoning

Claude Sonnet 4.5

Non-Reasoning

Weight access

Claude 3 Haiku

Proprietary

Claude Sonnet 4.5

Proprietary

License

Claude 3 Haiku

Proprietary

Claude Sonnet 4.5

Proprietary

Release date

Claude 3 Haiku

2024-03-01

Claude Sonnet 4.5

2025-09-01

If you already use one of these models

Deployment change
Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.01625 vs $0.195. Cache-heavy agent loop: $0.0675 vs $0.81.
Context tradeoff
Both models list 200K.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude 3 Haiku or Claude Sonnet 4.5?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude 3 Haiku or Claude Sonnet 4.5?

Claude 3 Haiku and Claude Sonnet 4.5 are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude 3 Haiku or Claude Sonnet 4.5?

Claude 3 Haiku is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude 3 Haiku or Claude Sonnet 4.5?

For the stated presets, chat costs $0.00088 on Claude 3 Haiku and $0.0105 on Claude Sonnet 4.5; repository review costs $0.01625 and $0.195; the cache-heavy agent loop costs $0.0675 and $0.81. Claude 3 Haiku does not fit this workload in one request. Claude Sonnet 4.5 does not fit this workload in one request. Claude 3 Haiku has no published cached-input rate, so cached tokens use its listed input rate. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude 3 Haiku or Claude Sonnet 4.5?

Both models list the same context window, 200K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence11 rows

Agentic

  • Terminal-Bench 2.0

    Claude 3 Haiku—
    Claude Sonnet 4.550%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude 3 Haiku—
    Claude Sonnet 4.561.4%
    Source

    Not directly comparable

  • VITA-Bench

    Claude 3 Haiku—
    Claude Sonnet 4.517.0%
    Source

    Not directly comparable

  • Gert Labs

    Claude 3 Haiku—
    Claude Sonnet 4.548.51%
    Source

    Not directly comparable

  • JobBench

    Claude 3 Haiku—
    Claude Sonnet 4.527.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude 3 Haiku—
    Claude Sonnet 4.577.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude 3 Haiku—
    Claude Sonnet 4.513.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude 3 Haiku—
    Claude Sonnet 4.583.4%
    Source

    Not directly comparable

Math

  • AIME 2025

    Claude 3 Haiku—
    Claude Sonnet 4.587%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude 3 Haiku—
    Claude Sonnet 4.513.495%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude 3 Haiku—
    Claude Sonnet 4.54.167%
    Source

    Not directly comparable

11 public results · 0 shared

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Last updated September 29, 2026