Supported Models
Model Catalog
syndicAI supports the latest frontier-class open-source models. All models are available on all tiers — you choose which model to run when creating your Squad Server. Many families ship in multiple quantization builds (AWQ, FP8, INT4, NVFP4, and others); the wizard shows only builds that fit your selected GPU hardware.
| Model | Total params | Active params | Context | Architecture | Reference VRAM |
|---|---|---|---|---|---|
| GLM-5.2 | 753B | ~39B | 262K | MoE | ~410GB (AWQ) · Hopper+ |
| MiniMax M3 | 453B | ~23B | 262K | MoE | ~258GB (NVFP4) · ~451GB (MXFP8) |
| Kimi K2.7 Code | 1T | ~32B | 262K | MoE | ~595GB (INT4) |
| Nex-N2 Pro | 397B | ~17B | 262K | MoE | ~210GB (INT4) · ~237GB (NVFP4) |
| MiniMax M2.5 | 230B | ~10B | 196K | MoE | ~122GB (AWQ) |
| Nex-N2 Mini | 35B | ~3B | 262K | MoE | ~35GB (FP8) |
| Qwen3.6 27B | 27B | 27B | 262K | Dense | ~28GB (FP8) |
SWE-bench Pro scores below are vendor-reported or catalog-sourced benchmarks for coding agent quality — useful for comparing families, not a guarantee of your exact workflow.
Why Mixture-of-Experts?
Most of our supported models use Mixture-of-Experts (MoE) architecture. In an MoE model, only a small fraction of the total parameters are activated for each token — typically 5–15% of the total. This means:
- Frontier-class quality: The full parameter count (230B, 453B, 753B, 1T) gives the model enormous knowledge capacity
- Efficient inference: Only the active parameters (3B–39B) consume compute per token, enabling fast generation
- Reasonable GPU requirements: A 230B MoE model can run on hardware that would struggle with a 230B dense model
This is why open-source models have caught up to proprietary ones — MoE architecture delivers the quality of massive models with the efficiency of smaller ones.
Model Details
GLM-5.2
Top of the catalog on SWE-bench Pro. GLM-5.2 is a 753B-parameter MoE model from Zhipu AI with approximately 39B active parameters per token.
- Context window: 262K tokens
- Strengths: Agentic coding, complex reasoning, tool use, multi-step refactors
- Benchmark highlights: SWE-bench Pro 62.1% (Z.ai)
- Available builds: FP8 (~756GB weights) and AWQ (~410GB weights)
- GPU note: Requires Hopper (H100) or Blackwell datacenter GPUs — Ampere and older are not supported due to sparse-MLA kernel requirements
MiniMax M3
MiniMax's latest flagship coding agent. 453B total parameters, ~23B active, with strong tool calling and long-context agent workflows.
- Context window: 262K tokens
- Strengths: Long-horizon agents, multi-file coding, frontier-class throughput on Blackwell
- Benchmark highlights: SWE-bench Pro 59.0% (MiniMax)
- Available builds: MXFP8 (~451GB) and NVFP4 (~258GB, Blackwell datacenter)
- GPU config: NVFP4 on 2× RTX PRO 6000 Blackwell is the practical sweet spot; MXFP8 needs more headroom
Kimi K2.7 Code
Moonshot AI's code-specialized MoE, built for long-horizon software engineering and agentic workflows.
- Context window: 262K tokens
- Strengths: Multi-step coding agents, large codebases, sustained tool loops
- Benchmark highlights: SWE-bench Pro 58.6% (proxied from Kimi K2.6 — no K2.7 Code score published yet)
- Available builds: Official INT4 (~595GB) and NVFP4 Blackwell build
- GPU config: 4× H100 80GB or 8× A100 80GB class hardware for the INT4 build
Nex-N2 Pro
Nex-AGI's highest-end open-weight coding model. 397B MoE with ~17B active parameters and hybrid linear-attention KV budgeting for long context.
- Context window: 262K tokens
- Strengths: Near-frontier coding quality on a more practical footprint (INT4 build)
- Benchmark highlights: SWE-bench Pro 58.8% (Nex-AGI vendor card)
- Available builds: BF16 (~794GB), INT4 (~210GB), NVFP4 (~237GB, Blackwell)
MiniMax M2.5
Our long-running reference config. Still an excellent default for squads that want proven stability and the lowest VRAM floor in the frontier tier.
- Context window: 196K tokens
- Strengths: Autocomplete, multi-file refactoring, code review, agentic coding workflows
- Benchmark highlights: SWE-bench Pro 55.4% (MiniMax)
- Available builds: AWQ (~122GB) and FP8 (~230GB)
- Reference config: 2× RTX PRO 6000 S with AWQ — well above 30 tok/s at ~$1.60/hr
Nex-N2 Mini
Compact Nex-AGI MoE for single-GPU tiers. 35B total, ~3B active — surprisingly capable for its size.
- Context window: 262K tokens
- Strengths: Lower cost, fast inference, native tool calling
- Benchmark highlights: SWE-bench Pro 50.2% (Nex-AGI vendor card)
- Available builds: FP8 (~35GB)
- GPU config: 1× datacenter GPU with ≥48GB VRAM
Qwen3.6 27B
Alibaba's dense instruction model — the lightweight option when you want strong tool calling without MoE overhead.
- Context window: 262K tokens
- Strengths: Fast inference, lower cost, good tool-use and coding for a dense 27B model
- Benchmark highlights: SWE-bench Pro 53.5% (aggregator estimate)
- Available builds: Official FP8 (~28GB)
- GPU config: 1× A100 80GB or 2× RTX 4090/5090 class GPUs
Choosing a Model
| Use case | Recommended model |
|---|---|
| Maximum SWE-bench quality | GLM-5.2 |
| Flagship agent + long context | MiniMax M3 |
| Long-horizon coding agents | Kimi K2.7 Code |
| Best value / reference config | MiniMax M2.5 AWQ |
| Near-frontier on fewer GPUs | Nex-N2 Pro (INT4) |
| Budget / single GPU | Qwen3.6 27B or Nex-N2 Mini |
| Longest context | Any 262K family (GLM-5.2, M3, Kimi, Nex-N2, Qwen3.6) |
You can change your model by creating a new Squad Server. Model changes require a new server because the GPU configuration and model weights differ.