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Table of Contents
Models
Selection Criteria
GLM-5
hf:zai-org/GLM-5
Price: $1.00/mtok in, $3.00/mtok out (beta pricing)
GLM-5 is in beta and is slated to be retired/proxied once GLM-5.1 exits beta. For new projects, prefer GLM-5.1.
GLM-5 was launched in beta on March 30, 2026, after SGLang made progress stabilizing GLM-5’s new architecture. It trades blows with proprietary models like Opus 4.6 and GPT-5.4 for coding and agentic tasks.
- Pros: Excellent at backend coding and long-horizon agentic work. Fewer total parameters than Kimi K2.5, so it can run at tp4 on B200 NVFP4 (4 GPUs vs K2.5’s required tp8), which is usually faster due to reduced NVLink overhead.
- Cons: Not as strong at UI/frontend work. Beta pricing is set high (impacts rate limits). Will be replaced by GLM-5.1.
Architecture Notes
GLM-5 has fewer total parameters than Kimi K2.5, making it more efficient to serve on B200 hardware at NVFP4 quantization. However, vLLM and SGLang support for GLM-5’s architecture was initially poor — Synthetic had to wait for upstream fixes before stable hosting was possible.
The model runs on SGLang (which is faster for the GLM series) and uses NVFP4 quantization on B200 GPUs. Each replica requires 4 B200 GPUs (tp4).
Deprecation Path
Based on public statements from Synthetic staff, the plan is:
1. Take GLM-5 out of beta, stop self-hosting GLM-4.7 2. Put [[:models:glm-5.1|GLM-5.1]] in beta 3. Once GLM-5.1 is out of beta, retire/proxy GLM-5
Old models are typically proxied to Fireworks or TogetherAI, although proxy duration depends on load since proxies are expensive.
See also: GLM-5.1 (the replacement), GLM-4.7 (the predecessor)
Kimi K2.5
hf:moonshotai/Kimi-K2.5
Price: $0.45/mtok in, $3.40/mtok out
hf:moonshotai/Kimi-K2.5 and hf:nvidia/Kimi-K2.5-NVFP4 are aliased internally and can be used interchangeably.
A powerful agentic model with above-average lateral thinking/debugging and great design skills.
- Pros: Solid code. Amazing at orchestrating other agents due to special “agent swarm” reinforcement learning (source). Only frontier class model on Synthetic with vision. Best model Synthetic has for UI work (probably because it was trained extensively with vision and to translate between visual input and code).
- Cons: Prone to outright laziness (keeping code for “backward compatibility”, marking things as “to implement later”) and thinking a bit too laterally. Should have an eye kept on her for longer tasks. Not quite as good as GLM-5 for backend work.
Although Kimi was trained with Agent Swarms, to get the same results as MoonshotAI you would have to use their proprietary swarms endpoint which is not available on Synthetic. However, similar results may be had using massively parallel sub-agents, or utilizing an SDK such as Swarms with various roles.
Load-Balanced Routing (NVFP4 + INT4)
Since February 15, 2026, Synthetic routes Kimi K2.5 requests between two hardware backends based on current load. Both model strings (hf:moonshotai/Kimi-K2.5 and hf:nvidia/Kimi-K2.5-NVFP4) may silently hit either backend. You don’t need to do anything differently.
- INT4 variant — runs on H200 GPUs (original Moonshot quant)
- NVFP4 variant — runs on B200 GPUs (Nvidia’s new near-lossless quant format, now U.S.-based)
Why: B200 capacity was sometimes overloaded while H200s sat with excess capacity. Routing between them based on load smooths this out and lets Synthetic scale Kimi via B200s going forward. Some INT4 capacity remains on reserved H200s (NVFP4 provides no perf advantage on H200 hardware).
Benchmark comparison (from Synthetic’s internal testing):
| Benchmark | INT4 | NVFP4 | Delta |
|---|---|---|---|
| AIME | 91.0 | 93.3 | NVFP4 +3.3 |
| Aider Polyglot | 74.4 | 71.1 | INT4 +3.3 |
| LiveCodeBench (subset) | — | — | NVFP4 +4.0 |
All within margin of error. NVFP4 mildly better on 2 of 3 benchmarks. Synthetic considers them equivalent for routing purposes.
Precision Mix Detail
Since April 7, 2026, Synthetic serves Kimi K2.5 from a mix of NVFP4 (B200s) and original INT4 (H200s). The /models API endpoint reports NVFP4 as the quant format, but your request may silently hit either backend based on current load.
When serving a model in multiple precisions, Synthetic reports the “least legitimate” (i.e., lab-released original) format in the /models endpoint. Since Moonshot’s original release was INT4 and Nvidia’s NVFP4 is a derived quant, NVFP4 is reported. However, INT4 capacity remains on reserved H200s where NVFP4 provides no performance advantage.
This means: even if you explicitly use hf:nvidia/Kimi-K2.5-NVFP4, your request may still be served by the INT4 variant on H200s during times of B200 load.
MiniMax M2.5
hf:MiniMaxAI/MiniMax-M2.5
Price: $0.40/mtok in, $2.00/mtok out
Currently the most capable middle-tier model on Synthetic for general agentic and coding tasks. Best used as a fast subagent orchestrated by a more powerful model like GLM 5 or Kimi K2.5.
- Pros: Very fast due to a very low active parameter count (10b). Pretty good at straightforward agentic tool use, agentic terminal use, and writing working, adequate code, as well as thoroughly exploring and writing reports on codebases or document collections.
- Cons: Will very easily get stuck in loops if it isn’t able to quickly debug an issue with its code — or its tools — in 1-2 turns. Requires detailed and thorough instructions to correctly execute the desired task (otherwise it will misinterpret what you mean, leave crucial things out, or just not understand the assignment).
Kimi K2-Thinking
hf:moonshotai/Kimi-K2-Thinking
Price: $0.60/mtok, $2.50/mtok
The previous most capable model on Synthetic before GLM 5 and Kimi K2.5 came around. Still by far the best writing model, though.
- Pros: Mostly just very good at writing, especially in a way that doesn’t have noticeable Claude-like LLM writing tells, and picking up on emotions and nuances.
- Cons: Writing isn’t always great at conveying coherent physical spaces or motions; can have continuity issues sometimes. Shouldn’t really be used for anything but the writing style at this point.
Nemotron 3 Super
hf:nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
Price: $0.30/mtok, $1.00/mtok
The most powerful budget model on Synthetic. Definitely worth using for agentic web search and report gathering, basic agentic terminal automation, as well as thread summary and title generation, and other basic housekeeping tasks you don’t need a frontier model for. Should not be allowed to touch code with a ten-foot pole.
- Pros: Very long context for such a cheap/small model (double the context of GPT-OSS 120b, which is the same size). Extremely, almost unnervingly fast. Does not really slow down over long contexts at all. Which is all thanks to the hybrid state space model architecture. Most powerful and capable fully open source model (source)
- Cons: Not really very flexible at problem solving. Can lose the plot pretty hard if set loose on a difficult problem for a very long time without feedback, although it doesn’t really context rot and is very tenacious, so it has that going for it. Probably shouldn’t be allowed to write code. Not that smart.
Embedding Models
Nomic Embed
hf:nomic-ai/nomic-embed-text-v1.5
If you have a Synthetic subscription, using an embedding model is free. Embedding models are commonly used in vector databases to provide effective semantic search over your data. Think of it as an easy way to browse through a hundred documents without giving an agent the entire context.
Nomic only works with text and is no longer state-of-the-art, so expect average performance.