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 | 1M | 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. Designed for a 1M-token context window, the largest in our catalog.
- Context window: 1M 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 | MiniMax M3 (1M); the 262K family covers GLM-5.2, 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.