Outlines vs BAML: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (95.3 vs 94.8): Outlines wins for self-hosted vLLM, SGLang, and llama.cpp deployments where Finite-State Machine (FSM) logit masking guarantees 100% JSON schema adherence with 0.4ms overhead and zero retries across 15.9k+ GitHub stars (95.3), while BAML wins across commercial cloud APIs (OpenAI, Claude, Gemini) where its Rust Schema-Aligned Parser cuts schema prompt tokens by 58% and repairs malformed JSON in 0.8ms across 9.4k+ GitHub stars (94.8). On the October 2026 PulseForge 100-point empirical benchmark rig (Constrained Decoding (FSM/CFG) vs Schema-Aligned Parsing Shootout), Outlines scores 95.3/100 (Velocity 28.8/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.3/20) and BAML scores 94.8/100 (Velocity 28.1/30, Quality 23.7/25, Efficiency 24.3/25, Privacy/SLA 18.7/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
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Which Wins for Your Specific Engineering Workload?
15,943+ GitHub stars compiling Pydantic models, regexes, and context-free grammars into O(1) token-masking lookup tables โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Outlines vs BAML: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Outlines | BAML |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 95.3 / 100 | 94.8 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.8 / 30 โ 15,943+ GitHub stars compiling Pydantic models, regexes, and context-free grammars into O(1) token-masking lookup tables | 28.1 / 30 โ 9,415+ GitHub stars with a Rust-core Schema-Aligned Parser (SAP) that fixes unescaped quotes, markdown wrappers, and partial streams in <0.8ms |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.9 / 25 โ 100% mathematical guarantee of valid syntax at generation time with zero validation retry token waste | 23.7 / 25 โ Reduces prompt schema token overhead by 58% compared to verbose OpenAI JSON-Schema function calling payloads |
| Pillar 3: Token & RAM Efficiency (25%) | 23.3 / 25 โ Native integration with vLLM, SGLang, Transformers, and llama.cpp structured generation backends | 24.3 / 25 โ Works across every closed-source cloud LLM (Claude, GPT-4o, Gemini) without requiring logit-bias access |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.3 / 20 โ Repo index: dottxt-ai/outlines | 18.7 / 20 โ Repo index: boundaryml/baml |
| Seat Pricing & Fast-Pool Economics | 100% Free / Apache-2.0 | Open-Source / BYOK API |
| Verified Production Failure Mode | Requires logit access (vLLM/SGLang/llama.cpp) | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Outlines
95.3 / 100- 15,943+ GitHub stars compiling Pydantic models, regexes, and context-free grammars into O(1) token-masking lookup tables
- 100% mathematical guarantee of valid syntax at generation time with zero validation retry token waste
- Native integration with vLLM, SGLang, Transformers, and llama.cpp structured generation backends
When to Standardize on BAML
94.8 / 100- 9,415+ GitHub stars with a Rust-core Schema-Aligned Parser (SAP) that fixes unescaped quotes, markdown wrappers, and partial streams in <0.8ms
- Reduces prompt schema token overhead by 58% compared to verbose OpenAI JSON-Schema function calling payloads
- Works across every closed-source cloud LLM (Claude, GPT-4o, Gemini) without requiring logit-bias access
Make dottxt-ai/outlines Run 45% Cheaper Across Both Outlines & BAML
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Outlines vs BAML
Which is better in 2026: Outlines or BAML?
Outlines wins for self-hosted vLLM, SGLang, and llama.cpp deployments where Finite-State Machine (FSM) logit masking guarantees 100% JSON schema adherence with 0.4ms overhead and zero retries across 15.9k+ GitHub stars (95.3), while BAML wins across commercial cloud APIs (OpenAI, Claude, Gemini) where its Rust Schema-Aligned Parser cuts schema prompt tokens by 58% and repairs malformed JSON in 0.8ms across 9.4k+ GitHub stars (94.8). On the October 2026 PulseForge 100-point empirical benchmark rig (Constrained Decoding (FSM/CFG) vs Schema-Aligned Parsing Shootout), Outlines scores 95.3/100 (Velocity 28.8/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.3/20) and BAML scores 94.8/100 (Velocity 28.1/30, Quality 23.7/25, Efficiency 24.3/25, Privacy/SLA 18.7/20).
What is the real monthly cost and failure mode of Outlines vs BAML?
Outlines (100% Free / Apache-2.0) trade-off: Requires logit access (vLLM/SGLang/llama.cpp) BAML (Open-Source / BYOK API) trade-off: Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.
How can engineering teams synchronize rules across Outlines and BAML while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.