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Model comparison

1-bit Bonsai 8B vs GPT-5.5

Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

1-bit Bonsai 8B

Prism ML

—

Evidence status unavailable

90% interval unavailable

GPT-5.5

OpenAI

72.0/100

Estimated · Public rank #11

90% interval 63.0–81.1

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 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

    GPT-5.5

    GPT-5.5 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

    No shared weighted benchmark basis supports a winner.

    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. 1-bit Bonsai 8B does not fit this workload in one request. 1-bit Bonsai 8B 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
0
1-bit Bonsai 8B only
0
GPT-5.5 only
33
Like-for-like categories
0 / 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.

Agentic

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
81.6
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
58.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
85.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
57.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
47.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
70.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
1-bit Bonsai 8B
Not measured
GPT-5.5
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.

A shared-evidence shape is not available.

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

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

1-bit Bonsai 8B
Self-hosted; infrastructure cost varies
Fits in one request
GPT-5.5
$0.02
Fits in one request

1-bit Bonsai 8B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

1-bit Bonsai 8B
Self-hosted; infrastructure cost varies
Fits in one request
GPT-5.5
$0.34
Fits in one request

1-bit Bonsai 8B has no comparable published API token rate.

Cache-heavy agent loop

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

1-bit Bonsai 8B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
GPT-5.5
$0.5
Fits in one request

1-bit Bonsai 8B does not fit this workload in one request. 1-bit Bonsai 8B 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.

Context window

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

1-bit Bonsai 8B

64K

Cached-input rate

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

1-bit Bonsai 8B

No comparable hosted API rate

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Documented inputs

1-bit Bonsai 8B

Not sourced

GPT-5.5

Not sourced

Documented outputs

1-bit Bonsai 8B

Not sourced

GPT-5.5

Not sourced

Provider availability

1-bit Bonsai 8B

Not sourced

GPT-5.5

Not sourced

Reasoning profile

1-bit Bonsai 8B

Non-Reasoning

GPT-5.5

Reasoning

Weight access

1-bit Bonsai 8B

Open Weight

GPT-5.5

Proprietary

License

1-bit Bonsai 8B

Open Weight

GPT-5.5

Proprietary

Release date

1-bit Bonsai 8B

2026-03-31

GPT-5.5

2026-04-23

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.5 has the larger documented window (1M).

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 evidence33 rows

Agentic

  • Terminal-Bench 2.0

    1-bit Bonsai 8B—
    GPT-5.582%
    Source

    Not directly comparable

  • CyberGym

    1-bit Bonsai 8B—
    GPT-5.581.8%
    Source

    Not directly comparable

  • BrowseComp

    1-bit Bonsai 8B—
    GPT-5.584.4%
    Source

    Not directly comparable

  • OSWorld-Verified

    1-bit Bonsai 8B—
    GPT-5.578.7%
    Source

    Not directly comparable

  • MCP Atlas

    1-bit Bonsai 8B—
    GPT-5.575.3%
    Source

    Not directly comparable

  • Toolathlon

    1-bit Bonsai 8B—
    GPT-5.555.6%
    Source

    Not directly comparable

  • τ²-bench results

    1-bit Bonsai 8B—
    GPT-5.598%
    Source

    Not directly comparable

  • Gert Labs

    1-bit Bonsai 8B—
    GPT-5.572.93%
    Source

    Not directly comparable

  • ResearchClawBench

    1-bit Bonsai 8B—
    GPT-5.517.0%
    Source

    Not directly comparable

  • OSWorld 2.0

    1-bit Bonsai 8B—
    GPT-5.513.0%
    Source

    Not directly comparable

  • JobBench

    1-bit Bonsai 8B—
    GPT-5.542.7%
    Source

    Not directly comparable

  • ExploitGym

    1-bit Bonsai 8B—
    GPT-5.513.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    1-bit Bonsai 8B—
    GPT-5.558.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    1-bit Bonsai 8B—
    GPT-5.582.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    1-bit Bonsai 8B—
    GPT-5.569.85%
    Source

    Not directly comparable

  • React Native Evals

    1-bit Bonsai 8B—
    GPT-5.584.7%
    Source

    Not directly comparable

  • cursorBench31

    1-bit Bonsai 8B—
    GPT-5.559.2%
    Source

    Not directly comparable

  • cursorBench32

    1-bit Bonsai 8B—
    GPT-5.558.4%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    1-bit Bonsai 8B—
    GPT-5.543.0%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 64K-128K

    1-bit Bonsai 8B—
    GPT-5.583.1%
    Source

    Not directly comparable

  • MRCR v2 128K-256K

    1-bit Bonsai 8B—
    GPT-5.587.5%
    Source

    Not directly comparable

  • ARC-AGI-2

    1-bit Bonsai 8B—
    GPT-5.585%
    Source

    Not directly comparable

  • ARC-AGI-3

    1-bit Bonsai 8B—
    GPT-5.50.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    1-bit Bonsai 8B—
    GPT-5.593.6%
    Source

    Not directly comparable

  • GPQA-D

    1-bit Bonsai 8B—
    GPT-5.593.6%
    Source

    Not directly comparable

  • HLE

    1-bit Bonsai 8B—
    GPT-5.552.2%
    Source

    Not directly comparable

  • HLE w/o tools

    1-bit Bonsai 8B—
    GPT-5.541.4%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    1-bit Bonsai 8B—
    GPT-5.551.7%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    1-bit Bonsai 8B—
    GPT-5.551.700%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    1-bit Bonsai 8B—
    GPT-5.535.400%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    1-bit Bonsai 8B—
    GPT-5.581.2%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    1-bit Bonsai 8B—
    GPT-5.583.2%
    Source

    Not directly comparable

  • OfficeQA Pro

    1-bit Bonsai 8B—
    GPT-5.554.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, 1-bit Bonsai 8B or GPT-5.5?

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, 1-bit Bonsai 8B or GPT-5.5?

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

Which is better for agentic tasks, 1-bit Bonsai 8B or GPT-5.5?

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, 1-bit Bonsai 8B or GPT-5.5?

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, 1-bit Bonsai 8B or GPT-5.5?

GPT-5.5 has the larger documented context window: 1M, compared with 64K.

Related comparisons

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Last updated July 29, 2026

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