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BenchLM
Model A
1-bit Bonsai 1.7B

Prism ML

—

Evidence status unavailable

90% interval unavailable

1-bit Bonsai 1.7B vs DeepSeek V4 Flash 0731

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

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 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

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 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 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K 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 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate.

    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 1.7B only
0
DeepSeek V4 Flash 0731 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 1.7B
Not measured
DeepSeek V4 Flash 0731
63.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
68.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
55.3
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
94.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
1-bit Bonsai 1.7B
Not measured
DeepSeek V4 Flash 0731
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 1.7B
Self-hosted; infrastructure cost varies
Fits in one request
DeepSeek V4 Flash 0731
$0.00028
Fits in one request

1-bit Bonsai 1.7B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

1-bit Bonsai 1.7B
Self-hosted; infrastructure cost varies
Does not fit in one request
DeepSeek V4 Flash 0731
$0.00784
Fits in one request

1-bit Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate.

Cache-heavy agent loop

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

1-bit Bonsai 1.7B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
DeepSeek V4 Flash 0731
$0.00616
Fits in one request

1-bit Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B 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 1.7B

32K

DeepSeek V4 Flash 0731

1M

DeepSeek models and pricing

Cached-input rate

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

1-bit Bonsai 1.7B

No comparable hosted API rate

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

Reasoning profile

1-bit Bonsai 1.7B

Non-Reasoning

DeepSeek V4 Flash 0731

Reasoning

Weight access

1-bit Bonsai 1.7B

Open Weight

DeepSeek V4 Flash 0731

Proprietary

License

1-bit Bonsai 1.7B

Open Weight

DeepSeek V4 Flash 0731

Proprietary

Release date

1-bit Bonsai 1.7B

2026-03-31

DeepSeek V4 Flash 0731

2026-07-31

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
DeepSeek V4 Flash 0731 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 1.7B—
    DeepSeek V4 Flash 073156.9%
    Source

    Not directly comparable

  • BrowseComp

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073173.2%
    Source

    Not directly comparable

  • HLE w/ tools

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073145.1%
    Source

    Not directly comparable

  • MCP Atlas

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073169%
    Source

    Not directly comparable

  • Toolathlon

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073147.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • CyberGym

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073176.7%
    Source

    Not directly comparable

  • Toolathlon-Verified

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073170.3%
    Source

    Not directly comparable

  • Agents' Last Exam

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073125.2%
    Source

    Not directly comparable

  • AutomationBench

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073125.1%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073191.6%
    Source

    Not directly comparable

  • Codeforces

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 07313052.0
    Source

    Not directly comparable

  • SWE-bench Verified

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073179%
    Source

    Not directly comparable

  • SWE-bench Pro

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073152.6%
    Source

    Not directly comparable

  • SWE Multilingual

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073173.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073156.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • NL2Repo

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073154.2%
    Source

    Not directly comparable

  • deepSwe

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073154.4%
    Source

    Not directly comparable

  • DSBench-FullStack

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073168.7%
    Source

    Not directly comparable

  • DSBench-Hard

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073159.6%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073178.7%
    Source

    Not directly comparable

  • CorpusQA 1M

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073160.5%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073186.2%
    Source

    Not directly comparable

  • SimpleQA

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073134.1%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073178.9%
    Source

    Not directly comparable

  • GPQA

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

  • GPQA-D

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

  • HLE

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073134.8%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073194.8%
    Source

    Not directly comparable

  • IMOAnswerBench

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073188.4%
    Source

    Not directly comparable

  • Apex

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073133.0%
    Source

    Not directly comparable

  • Apex Shortlist

    1-bit Bonsai 1.7B—
    DeepSeek V4 Flash 073185.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, 1-bit Bonsai 1.7B or DeepSeek V4 Flash 0731?

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 1.7B or DeepSeek V4 Flash 0731?

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 1.7B or DeepSeek V4 Flash 0731?

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 1.7B or DeepSeek V4 Flash 0731?

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 1.7B or DeepSeek V4 Flash 0731?

DeepSeek V4 Flash 0731 has the larger documented context window: 1M, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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