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Model comparison

BTL-4 vs MiniCPM5-1B

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

MiniCPM5-1B

OpenBMB

11.7/100

Estimated · Public rank #218

90% interval 1.8–21.6

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

2 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

    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. MiniCPM5-1B does not fit this workload in one request. BTL-4 has no comparable published API token rate. MiniCPM5-1B 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
2
BTL-4 only
1
MiniCPM5-1B only
12
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
BTL-4
Not measured
MiniCPM5-1B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
BTL-4
78.4
MiniCPM5-1B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
BTL-4
Not measured
MiniCPM5-1B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
BTL-4
Not measured
MiniCPM5-1B
44.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
BTL-4
Not measured
MiniCPM5-1B
33.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
BTL-4
Not measured
MiniCPM5-1B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
BTL-4
Not measured
MiniCPM5-1B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
BTL-4
Not measured
MiniCPM5-1B
58.5
Weighted basis
0 vs 2 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

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
MiniCPM5-1B
API rate not published
Fits in one request

BTL-4 has no comparable published API token rate. MiniCPM5-1B 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
MiniCPM5-1B
API rate not published
Fits in one request

BTL-4 has no comparable published API token rate. MiniCPM5-1B 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
MiniCPM5-1B
API rate not published
Does not fit in one request
Cached-input rate unavailable

MiniCPM5-1B does not fit this workload in one request. BTL-4 has no comparable published API token rate. MiniCPM5-1B 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

MiniCPM5-1B

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

MiniCPM5-1B

No comparable hosted API rate

Documented inputs

BTL-4

Not sourced

MiniCPM5-1B

Not sourced

Documented outputs

BTL-4

Not sourced

MiniCPM5-1B

Not sourced

Provider availability

BTL-4

Not sourced

MiniCPM5-1B

Not sourced

Reasoning profile

BTL-4

Reasoning

MiniCPM5-1B

Reasoning

Weight access

BTL-4

Open Weight

MiniCPM5-1B

Open Weight

License

BTL-4

Open Weight

MiniCPM5-1B

Open Weight

Release date

BTL-4

2026-08-05

MiniCPM5-1B

2026-05-25

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 evidence15 rows

Agentic

  • BFCL v4

    BTL-473.5%
    Source
    MiniCPM5-1B25.1%
    Source

    BTL-4 leads this result

Coding

  • SWE-bench Verified

    BTL-478.4%
    Source
    MiniCPM5-1B

    Not directly comparable

  • LiveCodeBench v6

    BTL-466.1%
    Source
    MiniCPM5-1B33.5%
    Source

    BTL-4 leads this result

  • LiveCodeBench Pro

    BTL-4
    MiniCPM5-1B22.7%
    Source

    Not directly comparable

Reasoning

  • BBH

    BTL-4
    MiniCPM5-1B71.9%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    BTL-4
    MiniCPM5-1B48.9%
    Source

    Not directly comparable

  • MMLU-Redux

    BTL-4
    MiniCPM5-1B70.1%
    Source

    Not directly comparable

  • GPQA-D

    BTL-4
    MiniCPM5-1B26.3%
    Source

    Not directly comparable

  • SuperGPQA

    BTL-4
    MiniCPM5-1B23.1%
    Source

    Not directly comparable

Math

  • AIME 2025

    BTL-4
    MiniCPM5-1B40.4%
    Source

    Not directly comparable

  • AIME26

    BTL-4
    MiniCPM5-1B40.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    BTL-4
    MiniCPM5-1B25.8%
    Source

    Not directly comparable

  • MATH-500

    BTL-4
    MiniCPM5-1B91.6%
    Source

    Not directly comparable

Instruction following

  • IFBench

    BTL-4
    MiniCPM5-1B46.7%
    Source

    Not directly comparable

  • IFEval

    BTL-4
    MiniCPM5-1B80.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, BTL-4 or MiniCPM5-1B?

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 MiniCPM5-1B?

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, BTL-4 or MiniCPM5-1B?

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 MiniCPM5-1B?

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 MiniCPM5-1B?

BTL-4 has the larger documented context window: 262K, compared with 131K.

Related comparisons

Last updated August 11, 2026

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