Coding
Directional only- BTL-4
- 78.4
- DeepSeek V4 Pro
- 65.3
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Model comparison
BTL-4 vs DeepSeek V4 Pro
Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
1 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 Pro
DeepSeek V4 Pro 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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
A complete comparable API-rate estimate is not available for both models.
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.
2 BTL-4 only
1 Shared
22 DeepSeek V4 Pro only
- Shared results
- 1
- BTL-4 only
- 2
- DeepSeek V4 Pro only
- 22
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
- DeepSeek V4 Pro
- 59.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- 41.3
- Weighted basis
- 0 vs 4 rows
- Reading
- Not comparable
Math
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- 31.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- BTL-4
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | BTL-4 | DeepSeek V4 Pro | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 78.4 | 65.3 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 59.1 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 41.3 | Not comparable0 vs 4 rows | Not comparable |
| Math | Not measured | 31.7 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE-bench Verified
Coding
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
- DeepSeek V4 Pro
- $0.00087
- Fits in one request
BTL-4 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
- DeepSeek V4 Pro
- $0.02436
- Fits in one request
BTL-4 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
- DeepSeek V4 Pro
- $0.01812
- Fits in one request
BTL-4 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.
SpecificationBTL-4DeepSeek V4 Pro
Context window
Maximum documented context; output-token limits may be lower.
BTL-4
DeepSeek V4 Pro
API model ID
BTL-4
Not sourced
DeepSeek V4 Pro
deepseek-v4-pro
DeepSeek models and pricingCached-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 cardDeepSeek V4 Pro
$0.003625 per 1M cached input tokens
Documented inputs
BTL-4
Not sourced
DeepSeek V4 Pro
Documented outputs
BTL-4
Not sourced
DeepSeek V4 Pro
Provider availability
BTL-4
Not sourced
DeepSeek V4 Pro
Preview · DeepSeek API, open weights
DeepSeek V4 Flash 0731 updateReasoning profile
BTL-4
Reasoning
DeepSeek V4 Pro
Non-Reasoning
Weight access
BTL-4
Open Weight
DeepSeek V4 Pro
Open Weight
License
BTL-4
Open Weight
DeepSeek V4 Pro
Open Weight
Release date
BTL-4
2026-08-05
DeepSeek V4 Pro
2026-04-24
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
- 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
- DeepSeek V4 Pro 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 evidence25 rows
Agentic
BFCL v4
BTL-473.5%SourceDeepSeek V4 Pro—Not directly comparable
Terminal-Bench 2.0
BTL-4—DeepSeek V4 Pro59.1%SourceNot directly comparable
MCP Atlas
BTL-4—DeepSeek V4 Pro69.4%SourceNot directly comparable
Toolathlon
BTL-4—DeepSeek V4 Pro46.3%SourceNot directly comparable
Claw-Eval
BTL-4—DeepSeek V4 Pro59.8%SourceNot directly comparable
Gert Labs
BTL-4—DeepSeek V4 Pro50.28%SourceNot directly comparable
ResearchClawBench
BTL-4—DeepSeek V4 Pro17.1%SourceNot directly comparable
Coding
SWE-bench Verified
BTL-478.4%SourceDeepSeek V4 Pro73.6%SourceBTL-4 leads this result
LiveCodeBench v6
BTL-466.1%SourceDeepSeek V4 Pro—Not directly comparable
LiveCodeBench Pass@1-COT
BTL-4—DeepSeek V4 Pro56.8%SourceNot directly comparable
SWE-bench Pro
BTL-4—DeepSeek V4 Pro52.1%SourceNot directly comparable
SWE Multilingual
BTL-4—DeepSeek V4 Pro69.8%SourceNot directly comparable
Terminal-Bench 2.0
BTL-4—DeepSeek V4 Pro59.1%SourceNot directly comparable
Reasoning
Knowledge
MMLU-Pro
BTL-4—DeepSeek V4 Pro82.9%SourceNot directly comparable
SimpleQA
BTL-4—DeepSeek V4 Pro45%SourceNot directly comparable
Chinese-SimpleQA
BTL-4—DeepSeek V4 Pro75.8%SourceNot directly comparable
GPQA
BTL-4—DeepSeek V4 Pro72.9%SourceNot directly comparable
GPQA-D
BTL-4—DeepSeek V4 Pro72.9%SourceNot directly comparable
HLE
BTL-4—DeepSeek V4 Pro7.7%SourceNot directly comparable
Math
Frequently asked questions
Which is better, BTL-4 or DeepSeek V4 Pro?
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 DeepSeek V4 Pro?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, BTL-4 or DeepSeek V4 Pro?
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 DeepSeek V4 Pro?
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 DeepSeek V4 Pro?
DeepSeek V4 Pro has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated August 11, 2026
Watch BTL-4 vs DeepSeek V4 Pro
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.