The Chinese labs shipped a run of strong open-weight models between April and June 2026. They don't do the same thing well, so the useful question isn't which one is best overall. It's which one fits the job in front of you. Here's how they actually sort out, with the benchmarks and the cost.
The short answer
If you want one name, it's GLM-5.2. Z.ai released it on June 13, and it became the first open-weight model to lead the Artificial Analysis Intelligence Index: 51, fourth overall, behind only Fable 5, Opus 4.8 and GPT-5.5. It scores 62.1 on SWE-bench Pro, runs a million-token context, and ships under MIT. We cover it in full in the GLM-5.2 deep dive.
That's your default. The rest depends on what you're doing: DeepSeek V4-Pro still wins raw algorithmic coding, V4-Flash wins on cost, and Kimi wins on long-context tool use.
DeepSeek V4-Pro: the coding and reasoning flagship
A 1.6T-parameter model with 49B active. It tops LiveCodeBench at 93.5 and holds a 3206 Codeforces rating, which is grandmaster territory and ahead of several closed APIs. On agent tasks it scores 67.9% on Terminal-Bench 2.0, and on hard reasoning 90.1% on GPQA Diamond.
This is the model for serious code generation, competitive-programming-grade logic, and agent loops where quality decides the outcome. On Turiloop it costs a fraction of closed-model rates, currently with a discount on every model. See current pricing.
DeepSeek V4-Flash: the cost killer
284B total, 13B active. It gives up a little capability (56.9% on Terminal-Bench) for a large drop in price: roughly $0.14 / $0.28 per million tokens, with cache hits near $0.014.
Point it at the work that doesn't need deep reasoning, which covers most high-volume jobs: classification, extraction, summarization, first drafts. On Turiloop the per-token cost rounds to nothing.
GLM-5.2: the new overall leader
Z.ai's GLM-5.2, from June 13, is the strongest open-weight model on the market. It scores 51 on the Artificial Analysis Intelligence Index (first among open weights) and 62.1 on SWE-bench Pro, beating DeepSeek V4-Pro's 55.4 on multi-file GitHub issues. It carries a million-token context and MIT-licensed weights. Under the hood it's a 744B MoE with about 40B active, which is how you get frontier-adjacent quality without frontier pricing.
Make it your default for serious coding and long-horizon agents. It's now both the all-rounder and the model to beat. If you don't need that edge, the older GLM-5.1 is a cheaper everyday option. On Turiloop you pay as you go with an international card. Full breakdown in the GLM-5.2 deep dive.
Kimi K2.6: the long-context agent
Moonshot built K2.6 for long documents and multi-step agents. It scores 66.7% on Terminal-Bench 2.0 and about 58.6% on SWE-Bench Pro, and its prompt-cache pricing (around $0.16/M on cache reads) makes a long, reused system prompt cheap to carry across a session.
Reach for it on large-context analysis, agentic pipelines, and anything that reuses a big stable prompt. On Turiloop it's cheaper than any closed frontier model.
MiniMax M2.5: the value generalist
MiniMax's M2 series is a cost-efficient generalist for chat and assistant workloads, and a low-cost all-rounder on Turiloop. Use it for general assistant traffic when you want something cheap and capable and don't want to think hard about the choice.
One line per job
- Overall best, coding and agents → GLM-5.2
- Hard algorithmic coding → DeepSeek V4-Pro
- High-volume cheap tasks → DeepSeek V4-Flash
- Everyday coding on a budget → GLM-5.1
- Long context and prompt reuse → Kimi K2.6
- General assistant traffic → MiniMax M2.5
The cheapest good setup is almost never one model. It's routing each job to the one that fits. On Turiloop they all sit behind one OpenAI-compatible key, so switching is a one-line model change, paid as you go with an international card.