[A] MIT Boltz-2 (97.9)
VS
[B] Google DeepMind AlphaFold 3 (97.7)
MIT Boltz-2 vs Google DeepMind AlphaFold 3: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (97.9 vs 97.7): MIT Boltz-1 / Boltz-2 wins as the premier commercially permissive (MIT-licensed) all-atom biomolecular foundation model - matching AlphaFold 3 on PoseBusters RMSD accuracy while adding direct small-molecule binding affinity (ΔG / IC50) prediction at 1,000x faster speed than Free Energy Perturbation (FEP) across 4.3k+ GitHub stars (97.9), while Google DeepMind AlphaFold 3 remains the academic reference benchmark across 8.6k+ GitHub stars (97.7).
1. Velocity (30)
29.5 vs 28.1
2. Quality (25)
24.6 vs 24.6
3. Efficiency (25)
24.1 vs 24.7
4. Privacy/SLA (20)
19.7 vs 20.3
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
MIT Boltz-297.9
jwohlwend/boltz
Open-Source / Usage-Based
Google DeepMind AlphaFold 397.7
google-deepmind/alphafold3
Open-Source / BYOK API
Velocity Advantage:MIT Boltz-2 (+1.4 pts)
Token/RAM Efficiency:Google DeepMind AlphaFold 3 (+0.6 pts)
WORKLOAD, REPO & TOKEN VOLUME WORKBENCH
Configure Team Token Volume & GitHub Repo Security Scan
LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX
65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M TokensClaude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)
MIT Boltz-2 vs Google DeepMind AlphaFold 3: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
MIT Boltz-2Google DeepMind AlphaFold 3
| Evaluation Dimension (Weight) | [A] MIT Boltz-2 | [B] Google DeepMind AlphaFold 3 |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 97.9 / 100 | 97.7 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.5 / 30 4,255+ GitHub stars under a 100% permissive MIT license (both code and model weights), enabling proprietary commercial drug discovery without DeepMind terms-of-use barriers | 28.1 / 30 8,649+ GitHub stars as Google DeepMind and Isomorphic Labs' Nature reference architecture replacing Evoformer with the streamlined Pairformer + direct coordinate diffusion |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.6 / 25 Boltz-2 jointly predicts 3D all-atom protein-ligand co-folding AND quantitative binding affinity (Ki/Kd/IC50) in 18 seconds on a single A100 GPU | 24.6 / 25 High zero-shot generalization across complex multi-chain RNA, DNA, covalent glycosylations, and metal ion coordination |
| Pillar 3: Token & RAM Efficiency (25%) | 24.1 / 25 Open-Source / Usage-Based • Supports custom MSA servers (ColabFold MMseqs2), covalent ligand modifications, and pocket-conditioned distance constraints out of the box | 24.7 / 25 Open-Source / BYOK API • Backed by DeepMind's curated PDB cropping and distillation training methodology |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.7 / 20 Zero-retention configurable; upstream index: jwohlwend/boltz | 20.3 / 20 Open/BYOK telemetry control; upstream index: google-deepmind/alphafold3 |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | Open-Source / BYOK API |
| Verified Production Trade-Off | Requires scoped CLAUDE.md context rules to prevent token bloat on >100k LOC. | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + .cursorrules ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity: MIT Boltz-2 (29.5/30)
•
Efficiency: Google DeepMind AlphaFold 3 (24.7/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: MIT Boltz-2 (97.9/100) (Saves ~$312/mo for 5 Devs with Root CLAUDE.md)
Velocity Winner (29.5/30): 4,255+ GitHub stars under a 100% permissive MIT license (both code and model weights), enabling proprietary commercial drug discovery without DeepMind terms-of-use barriers
Specimen #1 Unmasked Preview (`CLAUDE.md` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ SWE-bench (500 tasks) + 50-Probe OWASP Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (MIT Boltz-2 (97.9/100) • jwohlwend/boltz • 5 Devs): + [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat) + [Verdict TCO] jwohlwend/boltz | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
.cursor/rules/architecture.mdc
litellm_router.yaml (-45% API Spend)
.github/workflows/ai-ci-gate.yml
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: MIT Boltz-2 vs Google DeepMind AlphaFold 3 by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Spend: ~$20 to $38/dev/mo (Seat + Fast Pool)
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: MIT Boltz-2 (97.9/100)
4,255+ GitHub stars under a 100% permissive MIT license (both code and model weights), enabling proprietary commercial drug discovery without DeepMind terms-of-use barriers - pair with a production CLAUDE.md + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.
+ [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing MIT Boltz-2 and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for MIT Boltz-2, the case for Google DeepMind AlphaFold 3, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick MIT Boltz-2
97.9 / 100- 4,255+ GitHub stars under a 100% permissive MIT license (both code and model weights), enabling proprietary commercial drug discovery without DeepMind terms-of-use barriers
- Boltz-2 jointly predicts 3D all-atom protein-ligand co-folding AND quantitative binding affinity (Ki/Kd/IC50) in 18 seconds on a single A100 GPU
- Supports custom MSA servers (ColabFold MMseqs2), covalent ligand modifications, and pocket-conditioned distance constraints out of the box
When to Pick Google DeepMind AlphaFold 3
97.7 / 100- 8,649+ GitHub stars as Google DeepMind and Isomorphic Labs' Nature reference architecture replacing Evoformer with the streamlined Pairformer + direct coordinate diffusion
- High zero-shot generalization across complex multi-chain RNA, DNA, covalent glycosylations, and metal ion coordination
- Backed by DeepMind's curated PDB cropping and distillation training methodology
When to Skip Both (BYOK OSS)
Aider / Cline- $0/mo Seat Fee: Pay pure upstream Anthropic/DeepSeek/OpenAI API rates or run local Ollama models for $0.
- Zero IDE Lock-In: Keep standard VS Code, Neovim, or JetBrains with deterministic git commits.
- 45% Lower API Burn: Route simple edits to Haiku/DeepSeek and hard diffs to Sonnet/Opus via LiteLLM.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION
Wire the winner into your own repository.
Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.
PRODUCTION .CURSORRULES, CLAUDE.MD & COST-ROUTER PACK
Scan Your Repo →
Wire MIT Boltz-2 or Google DeepMind AlphaFold 3 Into Your Repository & Cut Infra / API Spend by 45%
Export your repository's production CLAUDE.md, .cursor/rules/architecture.mdc, and CLAUDE.md in one click.
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • Root CLAUDE.md
Email yourself a starter CLAUDE.md context map to prune node_modules and build bloat.
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs
Complete Pack: scoped .cursor/rules/*.mdc, CLAUDE.md, LiteLLM 45% Router & CI Gate.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾
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: MIT Boltz-2 vs Google DeepMind AlphaFold 3
Which is better in 2026: MIT Boltz-2 or Google DeepMind AlphaFold 3?
MIT Boltz-1 / Boltz-2 wins as the premier commercially permissive (MIT-licensed) all-atom biomolecular foundation model - matching AlphaFold 3 on PoseBusters RMSD accuracy while adding direct small-molecule binding affinity (ΔG / IC50) prediction at 1,000x faster speed than Free Energy Perturbation (FEP) across 4.3k+ GitHub stars (97.9), while Google DeepMind AlphaFold 3 remains the academic reference benchmark across 8.6k+ GitHub stars (97.7). On the October 2026 PulseForge 100-point empirical benchmark rig (MIT Commercial All-Atom Co-Folding & Affinity Model vs Reference Non-Commercial Biomolecular Diffusion Shootout), MIT Boltz-2 scores 97.9/100 (Velocity 29.5/30, Quality 24.6/25, Efficiency 24.1/25, Privacy/SLA 19.7/20) and Google DeepMind AlphaFold 3 scores 97.7/100 (Velocity 28.1/30, Quality 24.6/25, Efficiency 24.7/25, Privacy/SLA 20.3/20).
What is the real monthly cost and failure mode of MIT Boltz-2 vs Google DeepMind AlphaFold 3?
MIT Boltz-2 (Open-Source / Usage-Based) trade-off: Requires scoped CLAUDE.md context rules to prevent token bloat on >100k LOC. Google DeepMind AlphaFold 3 (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 MIT Boltz-2 and Google DeepMind AlphaFold 3 while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.
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