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BenchLM
Prism ML logo
Model A
1-bit Bonsai 8B

Prism ML

—

Evidence status unavailable

90% interval unavailable

1-bit Bonsai 8B vs Claude Opus 5

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Anthropic logo
Model B
Claude Opus 5

Anthropic

80.66/100

Supported · Public rank #4

90% interval 78.3–83.0

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.

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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 Opus 5

    Claude Opus 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

    1-bit Bonsai 8B is 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

    1-bit Bonsai 8B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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
Claude Opus 5 only
73
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
1-bit Bonsai 8B
Not ranked
Claude Opus 5
77.4
Supported · #2/151
Basis
BenchAlign lane · 0 vs 19 public rows
Reading
Not comparable

Coding

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
75.6
Supported · #3/183
Basis
BenchAlign lane · 0 vs 16 public rows
Reading
Not comparable

Reasoning

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
75.6
#13/22
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Knowledge

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
82.1
Supported · #3/181
Basis
BenchAlign lane · 0 vs 19 public rows
Reading
Not comparable

Math

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
88.7
#2/48
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
1-bit Bonsai 8B
Not ranked
Claude Opus 5
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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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
Claude Opus 5
$0.0175
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
Claude Opus 5
$0.325
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
Claude Opus 5
$0.45
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

Claude Opus 5

$0.5 per 1M cached input tokens

Claude API pricing

Reasoning profile

1-bit Bonsai 8B

Non-Reasoning

Claude Opus 5

Reasoning

Weight access

1-bit Bonsai 8B

Open Weight

Claude Opus 5

Proprietary

License

1-bit Bonsai 8B

Open Weight

Claude Opus 5

Proprietary

Release date

1-bit Bonsai 8B

2026-03-31

Claude Opus 5

2026-07-24

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
Claude Opus 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 evidence73 rows

Agentic

  • Terminal-Bench 3.0

    1-bit Bonsai 8B—
    Claude Opus 542.7%
    Source

    Not directly comparable

  • BrowseComp

    1-bit Bonsai 8B—
    Claude Opus 590.8%
    Source

    Not directly comparable

  • HLE w/ tools

    1-bit Bonsai 8B—
    Claude Opus 564.7%
    Source

    Not directly comparable

  • DeepSearchQA

    1-bit Bonsai 8B—
    Claude Opus 595.0%
    Source

    Not directly comparable

  • DRACO

    1-bit Bonsai 8B—
    Claude Opus 588.6%
    Source

    Not directly comparable

  • BrowseComp (10-agent, prerelease)

    1-bit Bonsai 8B—
    Claude Opus 593.6%
    Source

    Not directly comparable

  • OSWorld 2.0

    1-bit Bonsai 8B—
    Claude Opus 570.6%
    Source

    Not directly comparable

  • MCP Atlas

    1-bit Bonsai 8B—
    Claude Opus 585.8%
    Source

    Not directly comparable

  • MCP-Atlas claim coverage

    1-bit Bonsai 8B—
    Claude Opus 589.1%
    Source

    Not directly comparable

  • LAB all-pass (Anthropic harness)

    1-bit Bonsai 8B—
    Claude Opus 523.58%
    Source

    Not directly comparable

  • LAB criterion-pass (Anthropic harness)

    1-bit Bonsai 8B—
    Claude Opus 593.74%
    Source

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    1-bit Bonsai 8B—
    Claude Opus 511.7%
    Source

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    1-bit Bonsai 8B—
    Claude Opus 594.1%
    Source

    Not directly comparable

  • Toolathlon-Verified

    1-bit Bonsai 8B—
    Claude Opus 580.6%
    Source

    Not directly comparable

  • Toolathlon Verified Pass@3

    1-bit Bonsai 8B—
    Claude Opus 587.0%
    Source

    Not directly comparable

  • Toolathlon Verified Pass³

    1-bit Bonsai 8B—
    Claude Opus 573.1%
    Source

    Not directly comparable

  • Toolathlon Verified avg. turns

    1-bit Bonsai 8B—
    Claude Opus 523.5 turns
    Source

    Not directly comparable

  • AutomationBench

    1-bit Bonsai 8B—
    Claude Opus 526.0%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    1-bit Bonsai 8B—
    Claude Opus 584.6%
    Source

    Not directly comparable

Coding

  • Bug Hunt Bench

    1-bit Bonsai 8B—
    Claude Opus 527 fixes
    Source

    Not directly comparable

  • SWE-bench Verified

    1-bit Bonsai 8B—
    Claude Opus 596%
    Source

    Not directly comparable

  • SWE-bench Pro

    1-bit Bonsai 8B—
    Claude Opus 579.2%
    Source

    Not directly comparable

  • SWE Multilingual

    1-bit Bonsai 8B—
    Claude Opus 589.5%
    Source

    Not directly comparable

  • SWE Multimodal

    1-bit Bonsai 8B—
    Claude Opus 559.4%
    Source

    Not directly comparable

  • deepSwe

    1-bit Bonsai 8B—
    Claude Opus 568.8%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    1-bit Bonsai 8B—
    Claude Opus 553.4%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    1-bit Bonsai 8B—
    Claude Opus 563.6%
    Source

    Not directly comparable

  • FrontierSWE v2

    1-bit Bonsai 8B—
    Claude Opus 552.0%
    Source

    Not directly comparable

  • ProgramBench (episode 1)

    1-bit Bonsai 8B—
    Claude Opus 583.0%
    Source

    Not directly comparable

  • ProgramBench

    1-bit Bonsai 8B—
    Claude Opus 593.0%
    Source

    Not directly comparable

  • cursorBench32

    1-bit Bonsai 8B—
    Claude Opus 570.0%
    Source

    Not directly comparable

  • VulcanBench v3

    1-bit Bonsai 8B—
    Claude Opus 587.0%
    Source

    Not directly comparable

  • VulcanBench CII v1

    1-bit Bonsai 8B—
    Claude Opus 596.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    1-bit Bonsai 8B—
    Claude Opus 589.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    1-bit Bonsai 8B—
    Claude Opus 597.0%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    1-bit Bonsai 8B—
    Claude Opus 597.50%
    Source

    Not directly comparable

  • ARC-AGI-2

    1-bit Bonsai 8B—
    Claude Opus 590.4%
    Source

    Not directly comparable

  • ARC-AGI-3

    1-bit Bonsai 8B—
    Claude Opus 530.2%
    Source

    Not directly comparable

Knowledge

  • HLE

    1-bit Bonsai 8B—
    Claude Opus 564.7%
    Source

    Not directly comparable

  • HLE w/o tools

    1-bit Bonsai 8B—
    Claude Opus 556.3%
    Source

    Not directly comparable

  • HLE-Verified

    1-bit Bonsai 8B—
    Claude Opus 554.4%
    Source

    Not directly comparable

  • LABBench2

    1-bit Bonsai 8B—
    Claude Opus 584.2%
    Source

    Not directly comparable

  • HealthBench (raw)

    1-bit Bonsai 8B—
    Claude Opus 567.1%
    Source

    Not directly comparable

  • HealthBench (length-adjusted)

    1-bit Bonsai 8B—
    Claude Opus 557.8%
    Source

    Not directly comparable

  • HealthBench Professional

    1-bit Bonsai 8B—
    Claude Opus 559.8%
    Source

    Not directly comparable

  • HealthBench Professional (raw)

    1-bit Bonsai 8B—
    Claude Opus 573.4%
    Source

    Not directly comparable

  • BioMysteryBench (human-solvable)

    1-bit Bonsai 8B—
    Claude Opus 590.1%
    Source

    Not directly comparable

  • BioMysteryBench (human-difficult)

    1-bit Bonsai 8B—
    Claude Opus 549.4%
    Source

    Not directly comparable

  • SpatialBench Verified

    1-bit Bonsai 8B—
    Claude Opus 572.5%
    Source

    Not directly comparable

  • SingleCellBench

    1-bit Bonsai 8B—
    Claude Opus 560.6%
    Source

    Not directly comparable

  • ProteinGym Hard

    1-bit Bonsai 8B—
    Claude Opus 547.7%
    Source

    Not directly comparable

  • Protein Design

    1-bit Bonsai 8B—
    Claude Opus 542.5%
    Source

    Not directly comparable

  • Organic chemistry V2

    1-bit Bonsai 8B—
    Claude Opus 561.6%
    Source

    Not directly comparable

  • Protocols (troubleshooting)

    1-bit Bonsai 8B—
    Claude Opus 561.1%
    Source

    Not directly comparable

  • Protocols (understanding)

    1-bit Bonsai 8B—
    Claude Opus 578.4%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    1-bit Bonsai 8B—
    Claude Opus 593.4%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    1-bit Bonsai 8B—
    Claude Opus 591.6%
    Source

    Not directly comparable

Math

  • IMO 2026

    1-bit Bonsai 8B—
    Claude Opus 542/42
    Source

    Not directly comparable

  • RiemannBench (no tools)

    1-bit Bonsai 8B—
    Claude Opus 560.0%
    Source

    Not directly comparable

  • RiemannBench (tools)

    1-bit Bonsai 8B—
    Claude Opus 579.0%
    Source

    Not directly comparable

  • ArXivMath Jun. 2026 (no tools)

    1-bit Bonsai 8B—
    Claude Opus 590.8%
    Source

    Not directly comparable

  • ArXivMath Jun. 2026 (tools)

    1-bit Bonsai 8B—
    Claude Opus 591.3%
    Source

    Not directly comparable

Multilingual

  • GMMLU

    1-bit Bonsai 8B—
    Claude Opus 592.5%
    Source

    Not directly comparable

  • MILU

    1-bit Bonsai 8B—
    Claude Opus 592.1%
    Source

    Not directly comparable

  • INCLUDE

    1-bit Bonsai 8B—
    Claude Opus 589.8%
    Source

    Not directly comparable

Multimodal

  • Chartography (no tools)

    1-bit Bonsai 8B—
    Claude Opus 529.6%
    Source

    Not directly comparable

  • Chartography (tools)

    1-bit Bonsai 8B—
    Claude Opus 583.0%
    Source

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    1-bit Bonsai 8B—
    Claude Opus 50.366
    Source

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    1-bit Bonsai 8B—
    Claude Opus 50.821
    Source

    Not directly comparable

  • GDP.pdf (no tools)

    1-bit Bonsai 8B—
    Claude Opus 583.4%
    Source

    Not directly comparable

  • GDP.pdf (tools)

    1-bit Bonsai 8B—
    Claude Opus 585.5%
    Source

    Not directly comparable

  • OfficeQA

    1-bit Bonsai 8B—
    Claude Opus 578.1%
    Source

    Not directly comparable

  • OfficeQA Pro

    1-bit Bonsai 8B—
    Claude Opus 566.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, 1-bit Bonsai 8B or Claude Opus 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 Claude Opus 5?

1-bit Bonsai 8B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, 1-bit Bonsai 8B or Claude Opus 5?

1-bit Bonsai 8B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, 1-bit Bonsai 8B or Claude Opus 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 Claude Opus 5?

Claude Opus 5 has the larger documented context window: 1M, compared with 64K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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