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Model comparison

BTL-4 vs Claude Haiku 4.5

Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

BTL-4

Bad Theory Labs

Evidence status unavailable

90% interval unavailable

Claude Haiku 4.5

Anthropic

56.2/100

Estimated · Public rank #83

90% interval 44.7–67.7

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    BTL-4

    BTL-4 leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    BTL-4

    BTL-4 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • 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 Haiku 4.5 does not fit this workload in one request. BTL-4 has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
BTL-4 only
2
Claude Haiku 4.5 only
4
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like
BTL-4
78.4
Claude Haiku 4.5
73.3
Weighted basis
1 vs 1 rows
Reading
BTL-4 leads

Agentic

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
4.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
BTL-4
Not measured
Claude Haiku 4.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Claude Haiku 4.5
$0.0035
Fits in one request

BTL-4 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Claude Haiku 4.5
$0.065
Fits in one request

BTL-4 has no comparable published API token rate.

Cache-heavy agent loop

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

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Claude Haiku 4.5
$0.09
Does not fit in one request

Claude Haiku 4.5 does not fit this workload in one request. BTL-4 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Documented inputs

BTL-4

Not sourced

Claude Haiku 4.5

Not sourced

Documented outputs

BTL-4

Not sourced

Claude Haiku 4.5

Not sourced

Provider availability

BTL-4

Not sourced

Claude Haiku 4.5

Not sourced

Reasoning profile

BTL-4

Reasoning

Claude Haiku 4.5

Non-Reasoning

Weight access

BTL-4

Open Weight

Claude Haiku 4.5

Proprietary

License

BTL-4

Open Weight

Claude Haiku 4.5

Proprietary

Release date

BTL-4

2026-08-05

Claude Haiku 4.5

2025-10-15

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
BTL-4 has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence7 rows

Agentic

  • BFCL v4

    BTL-473.5%
    Source
    Claude Haiku 4.5

    Not directly comparable

  • JobBench

    BTL-4
    Claude Haiku 4.516.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    BTL-478.4%
    Source
    Claude Haiku 4.573.3%
    Source

    BTL-4 leads this result

  • LiveCodeBench v6

    BTL-466.1%
    Source
    Claude Haiku 4.5

    Not directly comparable

  • VulcanBench v3

    BTL-4
    Claude Haiku 4.578.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    BTL-4
    Claude Haiku 4.55.903%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    BTL-4
    Claude Haiku 4.52.083%
    Source

    Not directly comparable

Frequently asked questions

Which is better, BTL-4 or Claude Haiku 4.5?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, BTL-4 or Claude Haiku 4.5?

BTL-4 leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, BTL-4 or Claude Haiku 4.5?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, BTL-4 or Claude Haiku 4.5?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, BTL-4 or Claude Haiku 4.5?

BTL-4 has the larger documented context window: 262K, compared with 200K.

Related comparisons

Last updated August 11, 2026

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