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Model comparison

BTL-4 vs Claude Sonnet 4.6

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 Sonnet 4.6

Anthropic

64.4/100

Supported · Public rank #37

90% interval 52.0–76.9

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.

  • 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

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • 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 Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. BTL-4 has no comparable published API token rate.

    Confidence: rate-fallback

  • 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 Sonnet 4.6 only
19
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Directional only
BTL-4
78.4
Claude Sonnet 4.6
69.1
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
65.2
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
66.0
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
26.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
77.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
BTL-4
Not measured
Claude Sonnet 4.6
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 Sonnet 4.6
$0.0105
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 Sonnet 4.6
$0.195
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 Sonnet 4.6
$0.81
Does not fit in one request
Cached input priced at the published list-input rate

Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. 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.

API model ID

BTL-4

Not sourced

Claude Sonnet 4.6

Not sourced

Cached-input rate

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

BTL-4

No comparable hosted API rate

Bad Theory Labs BTL-4 model card

Claude Sonnet 4.6

Not published

Documented inputs

BTL-4

Not sourced

Claude Sonnet 4.6

Not sourced

Documented outputs

BTL-4

Not sourced

Claude Sonnet 4.6

Not sourced

Provider availability

BTL-4

Not sourced

Claude Sonnet 4.6

Not sourced

Reasoning profile

BTL-4

Reasoning

Claude Sonnet 4.6

Non-Reasoning

Weight access

BTL-4

Open Weight

Claude Sonnet 4.6

Proprietary

License

BTL-4

Open Weight

Claude Sonnet 4.6

Proprietary

Release date

BTL-4

2026-08-05

Claude Sonnet 4.6

2026-02-01

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 evidence22 rows

Agentic

  • BFCL v4

    BTL-473.5%
    Source
    Claude Sonnet 4.6—

    Not directly comparable

  • Terminal-Bench 2.0

    BTL-4—
    Claude Sonnet 4.659.1%
    Source

    Not directly comparable

  • OSWorld-Verified

    BTL-4—
    Claude Sonnet 4.672.1%
    Source

    Not directly comparable

  • Claw-Eval

    BTL-4—
    Claude Sonnet 4.667.8%
    Source

    Not directly comparable

  • CyberGym

    BTL-4—
    Claude Sonnet 4.665.2%
    Source

    Not directly comparable

  • Gert Labs

    BTL-4—
    Claude Sonnet 4.662.92%
    Source

    Not directly comparable

  • OSWorld 2.0

    BTL-4—
    Claude Sonnet 4.68.3%
    Source

    Not directly comparable

  • JobBench

    BTL-4—
    Claude Sonnet 4.636.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    BTL-478.4%
    Source
    Claude Sonnet 4.679.6%
    Source

    Claude Sonnet 4.6 leads this result

  • LiveCodeBench v6

    BTL-466.1%
    Source
    Claude Sonnet 4.6—

    Not directly comparable

  • SWE-Rebench

    BTL-4—
    Claude Sonnet 4.660.7%
    Source

    Not directly comparable

  • React Native Evals

    BTL-4—
    Claude Sonnet 4.680.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    BTL-4—
    Claude Sonnet 4.651.48%
    Source

    Not directly comparable

  • cursorBench31

    BTL-4—
    Claude Sonnet 4.648.8%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    BTL-4—
    Claude Sonnet 4.624.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    BTL-4—
    Claude Sonnet 4.689.9%
    Source

    Not directly comparable

  • SuperGPQA

    BTL-4—
    Claude Sonnet 4.695%
    Source

    Not directly comparable

  • MMLU-Pro

    BTL-4—
    Claude Sonnet 4.679.2%
    Source

    Not directly comparable

  • HLE

    BTL-4—
    Claude Sonnet 4.649%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    BTL-4—
    Claude Sonnet 4.632.400%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    BTL-4—
    Claude Sonnet 4.68.300%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    BTL-4—
    Claude Sonnet 4.677.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, BTL-4 or Claude Sonnet 4.6?

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 Sonnet 4.6?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, BTL-4 or Claude Sonnet 4.6?

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 Sonnet 4.6?

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 Sonnet 4.6?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 11, 2026

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