Capability
Unranked
field median 57.7
Not eligible for a public rank
Model profile · Bad Theory Labs
BTL-4
Released Aug 5, 2026 — see all recent releases
BTL-4 is tracked, but not publicly ranked yet. The profile exposes 3 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Data as of August 11, 2026 · How the score is built
Strongest published evidence
Agentic ranks #44. Particularly useful for coding agents, browser research, and computer-use workflows.
Validate before choosing
3 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
Self-hosted; infrastructure cost varies
input median $1
No comparable first-party hosted token rate
Speed
Not measured
field median 93 tok/s
Time to first token not measured
Context
262Ktokens
field median 201,500
Maximum output length is tracked separately
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic1/1 verified
- Coding2/2 verified
- ReasoningNot measured
- KnowledgeNot measured
- MathNot measured
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not publishedBad Theory Labs BTL-4 model card
- Context window
- 262KBad Theory Labs BTL-4 model card
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Apache-2.0 open-weight checkpoint for local and self-hosted deployment.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordBad Theory Labs BTL-4 model card
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #44 of 129Percentile 66thWeight 22%1 benchmarkVerified | 52.0 | #44 of 129 | 66th | 22% | 1 benchmark | Verified |
| CodingRank #52 of 135Percentile 62ndWeight 20%2 benchmarksVerified | 52.4 | #52 of 135 | 62nd | 20% | 2 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| MathWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score78.4% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap17.6 behind | WeightWeighted 16% | Provider exact |
| LiveCodeBench v6 | Score66.1% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.2% | Gap27.1 behind | WeightDisplay only | Provider exact |
Agentic1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BFCL v4Berkeley Function Calling Leaderboard v4 | Score73.5% | Versus best verified row Best verified: Qwen3.7 Max · 75.0% | Gap1.5 behind | WeightDisplay only | Provider exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Aug 5, 2026 · you are here
BTL-4Not publicly ranked · Price not listed
4 · v4
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track BTL-4, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
BTL-4 is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Apache-2.0 open-weight checkpoint for local and self-hosted deployment.
Official exact-value snapshot from Bad Theory Labs' BTL-4 Hugging Face model card. The model is fine-tuned from Ornith-1.0-35B for tool use, software engineering, and long-horizon agent work. BenchLM maps the reported SWE-bench Verified, LiveCodeBench v6, and BFCL v4 AST results; BFCL and LiveCodeBench are in-house runs using official scorers and full splits, while SWE-bench is reported with the official harness.
3 of 381 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Agentic at #44, while its lowest eligible position is Coding at #52. particularly useful for coding agents, browser research, and computer-use workflows.
Radar
Full release historyBTL-4 release history
BTL-4 released
Bad Theory Labs · Model release
Source confirmed
Frequently asked questions
How does BTL-4 perform overall in AI benchmarks?
BTL-4 has 3 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is BTL-4 good for coding and programming?
BTL-4 ranks #52 out of 135 eligible models for coding and programming, with a public category score of 52.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is BTL-4 good for agentic tool use and computer tasks?
BTL-4 ranks #44 out of 129 eligible models for agentic tool use and computer tasks, with a public category score of 52/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is BTL-4 open source?
BTL-4 is an open-weight model from Bad Theory Labs. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does BTL-4 have full benchmark coverage on BenchLM?
No. BTL-4 currently has 3 source-displayable rows across 381 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of BTL-4?
BTL-4 has a documented context window of 262K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.
Last updated August 11, 2026. Runtime fields remain blank until a sourced snapshot exists.
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