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5 evidence-reviewed models

Best Open Source AI Models for Coding

A current ranking of open-source and open-weight coding models, reviewed by exact artifact, benchmark evidence, license, weight access, and practical deployment.

Quick answer

GLM 5.2 is our current overall pick for long-horizon coding agents, repository work, and terminal tasks. Choose Qwen3 Coder 480B-A35B Instruct when Apache-2.0 terms and repository-scale context are the priority.

Read the selection methodology

Current ranking

Best Open Source Coding Models Ranked

This is an editorial ranking, not a disguised family-recency list or a composite of incompatible vendor benchmarks. Every entry is tied to an exact reviewed artifact.

#1

GLM 5.2

Z.ai

In Kilo

Best overall for long-horizon coding agents

Our current top editorial pick because its official release combines strong coding-agent results, a 1M-token context window, published weights, and permissive MIT terms.

Parameters
753B total
Context
1M tokens
License
MIT
Evidence snapshot
SWE-bench Pro: 62.1%
View GLM 5.2 model data
#2

Qwen3 Coder 480B-A35B Instruct

Qwen

In Kilo

Best Apache-2.0 model for repository-scale coding

A coding-specific checkpoint with published weights, Apache-2.0 terms, native 262K context, and first-party agentic coding guidance.

Parameters
480B total / 35B active
Context
262K tokens native; up to 1M with YaRN
License
Apache-2.0
Evidence snapshot
SWE-bench Pro: 38.7%
View Qwen3 Coder 480B-A35B Instruct model data
#3

Kimi K3

Moonshot AI

In Kilo

Best for long-horizon agentic coding under custom terms

A long-horizon agentic-coding model with published weights, Modified-MIT license terms, a native 1M-token context window, and vendor-reported terminal-agent coding results.

Parameters
2.8T total / 104B active
Context
1,048,576 tokens (1M)
Evidence snapshot
Terminal-Bench 2.1: 88.3%
View Kimi K3 model data
#4

Devstral Small 2505

Mistral AI

Source linked

Best reviewed option for a high-memory local workstation

The official release documents a 24B Apache-2.0 checkpoint that can run on a single RTX 4090 or a Mac with 32 GB memory.

Parameters
24B total
Context
See model source
License
Apache-2.0
Evidence snapshot
SWE-bench Verified: 46.8%
Open primary model source
#5

Laguna S 2.1

Poolside

In Kilo

Efficient MoE pick for long-horizon agentic coding

A compact MoE coding model with published weights, the permissive OpenMDW-1.1 license, a native 1M-token context window, and vendor-reported results competitive with larger models.

Parameters
118B total / 8B active
Context
1,048,576 tokens (1M)
Evidence snapshot
SWE-Bench Multilingual: 78.5%
View Laguna S 2.1 model data

Methodology

How We Rank Open Models

Models qualify only when we can review an exact coding-relevant artifact, its weight status, and its license. The order is editorial because coding benchmarks still use different agents, prompts, budgets, contexts, and attempt counts.

Benchmark and artifact snapshot reviewed . The hosted catalog refreshes independently and does not change the editorial review date.

  1. 1

    Artifact eligibility

    Published or gated weights, reviewable terms, and clear coding relevance.

  2. 2

    Coding evidence

    Agent and coding results, weighted by source quality and harness transparency.

  3. 3

    Deployment freedom

    License terms, weight access, hosted availability, and practical self-hosting.

  4. 4

    Editorial decision

    Workflow fit and evidence quality decide rank; incompatible scores are not averaged.

Evidence snapshot

Coding Benchmarks, Licenses, and Access

Results below retain their original harness labels. They are evidence for each checkpoint, not a normalized cross-vendor league table.

ModelLicenseWeight accessPublished evidenceHarness note
GLM 5.2MITavailableSWE-bench Pro: 62.1%OpenHands with the release prompt and a 400K context window
Qwen3 Coder 480B-A35B InstructApache-2.0availableSWE-bench Pro: 38.7%Scale AI public evaluation listed on the model card
Kimi K3Modified MIT (Kimi K3 License)availableTerminal-Bench 2.1: 88.3%Kimi Code harness, max reasoning effort
Devstral Small 2505Apache-2.0availableSWE-bench Verified: 46.8%OpenHands release evaluation
Laguna S 2.1OpenMDW-1.1availableSWE-Bench Multilingual: 78.5%Poolside release evaluation

Vendor-reported results can vary with scaffold, prompt, tool permissions, context, compute budget, and number of attempts. Open the linked source before making a procurement or deployment decision.

Open Source vs Open Weight

Most current coding leaders are more precisely described as open-weight models. Weight access alone does not settle rights to modify, redistribute, fine-tune, or use an artifact commercially.

Compare definitions and license checks

Local, Hosted, or BYOK

Run a reviewed artifact locally when your hardware fits, use a hosted Kilo route, or connect a provider key. Large MoE checkpoints usually need server-class inference even when their active parameter count looks small.

Choose the Right Ranking

This page owns evidence-reviewed open-model recommendations. Use these focused pages for live, free, new-release, or hardware-specific questions.

Primary Model Sources

Review the exact artifact and terms before deploying. A lab name or similar model slug is never treated as evidence that another checkpoint is open.

Open Source AI Models FAQ

What is the best open source AI model for coding in 2026?+

GLM 5.2 is our current overall editorial pick for long-horizon coding agents, repository work, and terminal tasks. The best choice still depends on your license, hardware, latency, and workflow requirements.

Are open-weight AI models the same as open-source AI models?+

Not always. Open-weight means the model weights are available under stated terms. Open-source AI has broader requirements around the freedoms and information needed to use, study, modify, and share the system. This ranking uses artifact-level classifications and shows the license for every model.

Which open model is best for local coding?+

Devstral Small 2505 is the strongest reviewed local-workstation option in this ranking. Local fit depends on the exact artifact, quantization, runtime, context length, and available memory.

How are these open source coding models ranked?+

This is an editorial ranking based on coding-agent evidence, benchmark quality, license and deployment freedom, practical availability, efficiency, and maintenance. Vendor results are not merged into a single composite score when harnesses differ.

Which ranked open models are available in Kilo?+

The current Kilo model feed matched these reviewed artifacts: GLM 5.2, Qwen3 Coder 480B-A35B Instruct, Kimi K3, Laguna S 2.1. Availability can change, so unmatched entries link to their primary model source instead.

Related Model Guides

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