Best Open Source Coding Models

How this ranking is produced

Recommendation engine phase-2.0.0, 15 benchmarks evaluated, aggregate freshness fresh. Computed from the canonical ModelRefs registry when this page was built on 2026-09-04, and recomputed on every deploy.

Each candidate below is a provisional fit signal under the stated constraints, not a guarantee, a certification, or a final ranking. Fit scores compare models against this use case's capability weights using current ModelRefs evidence; they are not accuracy rates, benchmark results, or production-readiness claims.

What we measure for Coding Copilot

Coding

100%

Latency

50%

Structured Output

40%

Weights derived from the ModelRefs capability ontology. Scores sourced from primary benchmark leaderboards where available; expansion entries carry lower confidence (0.65).

Ranked candidates

1. #1 Phi-4 — Microsoft

   Fit score 35 out of 100.

   • Strong cost efficiency (100/100).
   • Strong multilingual (81/100).
   • Open-weight (MIT).
   • Caution: Weak hallucination resistance (3/100).
   • Caution: Overall fit moderate (35/100) — consider alternatives.

   Capability evidence

   • Latency — evidence confidence 40% — Underperforms on latency (0/100).
   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

   Confidence: 40% · Freshness: Stale 0% · Evidence: 33% · Benchmarks: 33% · Stability: 88%

2. #2 DeepSeek R1 — DeepSeek

   Fit score 35 out of 100.

   • Strong reasoning (76/100).
   • Strong cost efficiency (75/100).
   • Open-weight (MIT).
   • Caution: Overall fit moderate (35/100) — consider alternatives.

   Capability evidence

   • Latency — evidence confidence 40% — Underperforms on latency (0/100).
   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 33% · Benchmarks: 33% · Stability: 38%

3. #3 QwQ 32B — Alibaba

   Fit score 33 out of 100.

   • Strong cost efficiency (91/100).
   • Open-weight (Apache-2.0).
   • Caution: Overall fit moderate (33/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Latency — evidence confidence 40% — Underperforms on latency (0/100).
   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 33% · Benchmarks: 33% · Stability: 13%

4. #4 Llama 4 Scout — Meta

   Fit score 17 out of 100.

   • Strong cost efficiency (94/100).
   • Strong ocr (89/100).
   • Strong multimodal (76/100).
   • Open-weight (Llama 4 Community).
   • Caution: Weak coding (33/100).
   • Caution: Overall fit moderate (17/100) — consider alternatives.

   Capability evidence

   • Coding — evidence confidence 40% — Underperforms on coding (33/100). via livecodebench
   • Latency — evidence confidence 40% — Underperforms on latency (0/100).
   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 33% · Benchmarks: 33% · Stability: 100%

5. #5 Qwen 2.5 Coder 32B — Alibaba

   Fit score 17 out of 100.

   • Strong cost efficiency (96/100).
   • Open-weight (Apache-2.0).
   • Caution: Weak coding (31/100).
   • Caution: Overall fit moderate (17/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Coding — evidence confidence 40% — Underperforms on coding (31/100). via livecodebench
   • Latency — evidence confidence 40% — Underperforms on latency (0/100).
   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 33% · Benchmarks: 33% · Stability: 13%

Knowledge Graph signal

Independent cross-validation from the ModelRefs semantic graph. Models below were identified via graph traversal of benchmark to capability to use-case edges — a separate signal from the recommendation engine above.

Capability fit describes how strongly a model's measured capabilities match this use case. It is not an accuracy rate or production-readiness guarantee. Evidence confidence describes how complete and well-supported the evidence behind that fit is; missing or stack-level requirements lower confidence.

1. #1 GPT-5 — evidence confidence 70%
2. #2 GPT-5 Mini — evidence confidence 70%
3. #3 Phi-4 — evidence confidence 70%
4. #4 DeepSeek R1 — evidence confidence 70%
5. #5 Gemini 2.5 Pro — evidence confidence 70%
6. #6 Claude 3.7 Sonnet — evidence confidence 70%

Limits of this ranking

Coverage is uneven. A model ranks only where ModelRefs holds benchmark-eligible evidence for the capabilities this use case requires, so a strong model with thin evidence can rank low or be absent entirely. Confidence, evidence, and benchmark-coverage figures beside each candidate say how well supported its position is — read them before acting on the order.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Best Open Source Coding Models.

• Browse models
• Review providers
• Inspect benchmarks
• Explore workflows
• Learn AI concepts
• Read decision guides
• Review trust and methodology
• Start a decision path