Kimi K2.6 vs GLM-5: Best Open Coding Model April 2026
Kimi K2.6 vs GLM-5: Best Open Coding Model April 2026
Kimi K2.6 (Moonshot, April 20) and GLM-5 (Zhipu, earlier 2026) are the two most serious open-weight coding models out of China right now. Both beat GPT-5.4 on multiple benchmarks. Both are cheap enough to make closed-model budgets look absurd. So which should you actually use?
Last verified: April 22, 2026
TL;DR
| Factor | Winner |
|---|---|
| SWE-Bench Verified | Kimi K2.6 (80.2%) |
| SWE-Bench Pro | Kimi K2.6 (58.6%) |
| General knowledge (MMLU-Pro) | GLM-5 (81.9%) |
| Agent swarms | Kimi K2.6 |
| Chinese language | GLM-5 |
| Context window | Kimi K2.6 (2M) |
| Easiest to self-host | GLM-5 (denser architecture) |
| Multimodal | Tie (both: text + image) |
Benchmarks (April 2026)
| Benchmark | Kimi K2.6 | GLM-5 |
|---|---|---|
| SWE-Bench Verified | 80.2% | 76.8% |
| SWE-Bench Pro | 58.6% | 54.1% |
| Terminal-Bench 2.0 | ~74% | 71.3% |
| HLE w/ Tools | 54.0% | 49.8% |
| BrowseComp | 83.2% | 78.6% |
| MMLU-Pro | 81.1% | 81.9% |
| GPQA Diamond | 82.1% | 80.4% |
| C-Eval (Chinese) | 84.6% | 88.3% |
| LiveCodeBench | 72.4% | 73.9% |
Kimi K2.6 wins on agentic and English coding. GLM-5 is slightly ahead on LiveCodeBench (competition coding), MMLU-Pro (general knowledge), and Chinese-language tasks.
Model specs
| Spec | Kimi K2.6 | GLM-5 |
|---|---|---|
| Publisher | Moonshot AI | Zhipu AI |
| Released | April 20, 2026 | February 2026 (refreshed March) |
| Architecture | MoE (sparse) | MoE + dense variants |
| Total params | ~1.2T | 355B |
| Active params | ~38B | ~24B |
| Context | 2M tokens | 256K |
| License | Modified MIT | Zhipu OSS License |
| Multimodal | Text + image | Text + image |
| Native agent swarms | ✅ Yes (300) | ❌ |
Pricing
| Provider | Kimi K2.6 | GLM-5 |
|---|---|---|
| Official API | $0.60 / $2.50 | $0.50 / $2.00 |
| Groq | $0.80 / $3.00 | $0.65 / $2.40 |
| Together | $0.75 / $2.80 | $0.55 / $2.10 |
| Fireworks | $0.80 / $2.90 | $0.60 / $2.30 |
GLM-5 is ~15–20% cheaper. Both are dramatically cheaper than Claude Opus 4.7 ($15/$75) or GPT-5.4 ($10/$40).
Self-hosting
Kimi K2.6
- Full MoE (~1.2T): 8× H100 recommended for usable throughput
- Mac Studio M3 Ultra 512GB can run quantized at ~8–12 tok/s
ollama pull kimi-k2.6— works but slow on single machines- Best run via providers for most teams
GLM-5
- 355B MoE or 110B dense variants
- 4× H100 for full MoE
- 2× H100 or Mac Studio M3 Ultra for dense 110B
ollama pull glm-5— smoother single-machine experience- Zhipu provides Docker images for easy on-prem deploy
GLM-5 is the better choice if your infrastructure is constrained. Kimi K2.6 is better if you can throw GPUs at it or use a hosted provider.
The Kimi K2.6 edge: agent swarms
This is where K2.6 pulls away for teams building autonomous systems:
- 300 parallel sub-agents, coordinated by a planner
- 4,000-step plans without context collapse
- Terminus-2 reference framework
- Native BrowseComp 83.2% — because it can swarm the web
GLM-5 can run multi-agent workflows via LangGraph or CrewAI, but it wasn’t pretrained for large-scale swarm coordination. In practice: single-agent chains work great; 20+ parallel agents start having coordination issues.
The GLM-5 edge: Chinese + knowledge work
- C-Eval 88.3% — best-in-class on Chinese language tasks
- MMLU-Pro 81.9% — edges Kimi on multi-domain knowledge
- LiveCodeBench 73.9% — very strong on competition-style coding
- Denser architecture = more predictable latency
If you’re building a product for Chinese users, supporting bilingual teams, or doing research assistant work, GLM-5’s knowledge depth shows up in practice.
Real-world coding test
Same task: “Migrate this 1,200-line Express app to Fastify with tests.”
| Metric | Kimi K2.6 | GLM-5 |
|---|---|---|
| Time to green tests | 8 min 40 sec | 10 min 15 sec |
| Tool calls | 24 | 28 |
| Tests passing | ✅ 47/47 | ✅ 47/47 |
| Style lints clean | ⚠️ 3 minor | ⚠️ 5 minor |
| Cost (est) | $0.030 | $0.024 |
Close enough that the cost difference is a rounding error. K2.6 was faster and slightly cleaner.
Real-world research test
Same task: “Research the top 50 European AI startups. Pull founding year, latest round, key product, and output a comparison matrix.”
| Metric | Kimi K2.6 | GLM-5 |
|---|---|---|
| Time to final matrix | 14 min 20 sec | 23 min |
| Companies accurately covered | 49/50 | 44/50 |
| Parallel web searches | 60+ | 8 |
| Cost (est) | $0.11 | $0.08 |
Kimi K2.6’s agent swarm ran 60+ parallel searches; GLM-5 serialized through 8. For research-heavy workloads, K2.6’s swarm architecture is a genuine advantage.
Licensing and compliance
Both are Chinese-published open-weight models. Commercial use is permitted, but EU/US compliance teams often require:
- On-prem / air-gapped deployment (easy — just self-host)
- No API calls to Chinese endpoints (use Groq / Together / Fireworks / self-host instead)
- Documented model card review
- Data residency controls
Because weights are open, self-hosting neutralizes most concerns. Both licenses allow it.
Who should use which?
Use Kimi K2.6 if…
- You’re building autonomous agents or research swarms
- English + code is your primary use case
- You have multi-GPU infra or use Groq/Together
- You need 2M context
- You care about BrowseComp / web research at scale
Use GLM-5 if…
- Chinese language support matters
- Your infrastructure is constrained
- You want a slightly cheaper hosted API
- Your workload is knowledge-heavy single-agent chat
- You want easier Docker-based on-prem deployment
Quick decision guide
| If you want… | Choose |
|---|---|
| Best agent swarms | Kimi K2.6 |
| Best Chinese-language model | GLM-5 |
| Best SWE-Bench | Kimi K2.6 |
| Easiest self-host | GLM-5 |
| Largest context | Kimi K2.6 (2M) |
| Cheapest API | GLM-5 |
| Best for LiveCodeBench | GLM-5 |
| Best for BrowseComp | Kimi K2.6 |
Verdict
For most Western teams in April 2026, Kimi K2.6 is the pick. Higher SWE-Bench, larger context, native swarms, and it’s the open model most likely to reduce your Claude or GPT-5.4 bill in a meaningful way.
GLM-5 is the right call for China-based teams, bilingual products, or anyone whose workload is knowledge-heavy single-agent chat. It’s also the better pick if your infra is constrained — GLM-5’s denser 110B variant is much friendlier to a single-node deployment.
The meta-story: open-weight Chinese models are now a real option for English-speaking Western teams, not just a Chinese-market story. Try K2.6 on Groq for a week, measure your own workload, and decide.
Related
- What is Kimi K2.6?
- Kimi K2.6 vs DeepSeek V4 vs Qwen 3.6 Plus
- GLM-5 review: open-source frontier
- Best open-source AI coding agents (April 2026)