DeepSeek V4 vs Kimi K2.6 vs GLM-5.1: Open Models April 2026
DeepSeek V4 vs Kimi K2.6 vs GLM-5.1: Open Models April 2026
DeepSeek V4 dropped April 24, 2026. The open-weight frontier just reshuffled — again. Here’s how the three current open-weight leaders stack up as of April 25, 2026.
Last verified: April 25, 2026
TL;DR
| DeepSeek V4-Pro | Kimi K2.6 | GLM-5.1 | |
|---|---|---|---|
| Maker | DeepSeek (Hangzhou) | Moonshot AI | Z.ai (Zhipu) |
| Total params | 1.6T MoE | 1T MoE | 800B MoE (est.) |
| Active per token | 49B | ~32B | ~25B |
| Context window | 1M | 256K | 1M |
| SWE-bench Verified | 80.6% | 80.2% | 78.4% |
| SWE-Bench Pro | 47.2% | 44.1% | 49.8% |
| Terminal-Bench | 67.9% | 64.1% | 61.3% |
| API price ($/M in/out) | 1.74 / 3.48 | 0.60 / 2.50 | 0.30 / 1.10 |
| License | Custom (commercial OK) | Apache 2.0 | Apache 2.0 |
DeepSeek V4-Pro — the new open-weight king
Released: April 24, 2026 (preview)
V4-Pro is the most capable open-weight model on the market right now. It’s within 0.2 points of Claude Opus 4.7 on SWE-bench Verified, beats it on Terminal-Bench and LiveCodeBench, and trails only Gemini 3.1 Pro on world knowledge.
Strengths:
- Best open-weight reasoning and general intelligence
- 1M token context (full monorepos, full books)
- Aggressive pricing — $3.48/M output is unprecedented at this quality
- Officially supported on Huawei Ascend 950 supernodes (vLLM-Ascend)
- Open weights on Hugging Face (deepseek-ai/DeepSeek-V4-Pro)
Weaknesses:
- Custom license (not OSI-compliant; Apache 2.0 / MIT models are stricter “open source”)
- Smaller MCP/tool ecosystem than Anthropic stack
- Tool-calling reliability still ~5 points behind closed-frontier models
- Self-hosting V4-Pro at full quality needs multi-node infra
Best for: Teams that want the highest open-weight quality, 1M context use cases, China-friendly deployments, anyone running >100M tokens monthly.
Kimi K2.6 — the agentic swarm specialist
Released: February 2026
Kimi K2.6 is Moonshot AI’s flagship and the best open-weight model for parallel multi-agent work. Its standout feature is native support for ~300 concurrent sub-agents within a single workflow — something no other open or closed model matches today.
Strengths:
- 300+ parallel sub-agents in a single agent loop
- Apache 2.0 — fully open, no commercial restrictions
- Strong tool-calling reliability (one of the best open models for tool use)
- Excellent agentic coding (80.2% SWE-bench Verified)
- Strong long-form writing
Weaknesses:
- 256K context (vs 1M for V4-Pro and GLM-5.1)
- Lower world-knowledge benchmarks than V4-Pro
- Slightly more expensive on output ($2.50 vs Pro $3.48 — but Kimi has a smaller param count, so this is mostly a positioning choice)
- Smaller community than DeepSeek
Best for: Multi-agent swarms, complex tool-orchestration agents, teams that need true Apache 2.0 licensing, research workflows.
GLM-5.1 — the production-patch champion
Released: March 2026
Zhipu AI / Z.ai’s GLM-5.1 is quieter in marketing but punches above its weight on a critical benchmark: SWE-Bench Pro, which measures production-grade patch quality on real GitHub issues. GLM-5.1 leads the open-weight pack here at 49.8%.
Strengths:
- Best open-weight model on SWE-Bench Pro (production patches)
- 1M context window
- Cheapest of the three on API ($0.30/$1.10)
- Apache 2.0 license
- Smallest deployable footprint — fits on 8×H100 or single H200 with INT4
- Strong English/Chinese bilingual performance
Weaknesses:
- Lower SWE-bench Verified score (78.4% vs V4 80.6%)
- Smaller ecosystem than DeepSeek
- Less name recognition outside China — fewer English tutorials
- Occasional tool-calling format quirks
Best for: Teams optimizing for production patch quality, cost-conscious self-hosting, anyone who needs full Apache 2.0 + 1M context.
Side-by-side benchmarks
| Benchmark | V4-Pro | Kimi K2.6 | GLM-5.1 |
|---|---|---|---|
| MMLU-Pro | 83.2% | 80.4% | 79.1% |
| GPQA Diamond | 78.6% | 75.2% | 73.4% |
| SWE-bench Verified | 80.6% | 80.2% | 78.4% |
| SWE-Bench Pro | 47.2% | 44.1% | 49.8% |
| LiveCodeBench | 93.5% | 89.1% | 87.4% |
| Terminal-Bench 2.0 | 67.9% | 64.1% | 61.3% |
| τ²-Bench (agents) | 71.4% | 74.8% | 68.2% |
| AIME 2026 (math) | 88.4% | 84.1% | 82.7% |
Bottom line: V4-Pro wins most categories. Kimi K2.6 wins on multi-agent (τ²-Bench). GLM-5.1 wins on production patches (SWE-Bench Pro).
Pricing for 100M tokens/month (50/50 split)
| Model | API monthly cost |
|---|---|
| GLM-5.1 | $70 |
| Kimi K2.6 | $155 |
| DeepSeek V4-Flash | $21 |
| DeepSeek V4-Pro | $261 |
| Claude Sonnet 4.6 | $375 |
If pure cost is the priority, V4-Flash dominates. If you need full Pro-tier quality, GLM-5.1 is the budget pick at $70/M tokens.
Decision tree
- Need the best open-weight quality? → DeepSeek V4-Pro
- Need lowest cost with 1M context? → DeepSeek V4-Flash
- Building agentic swarms / multi-agent workflows? → Kimi K2.6
- Need Apache 2.0 license? → Kimi K2.6 or GLM-5.1
- Production patch quality matters most? → GLM-5.1
- Self-hosting on modest hardware? → GLM-5.1
- Need Chinese-market deployment + Huawei Ascend? → DeepSeek V4
What to actually do
For most builders in late April 2026:
- Default to DeepSeek V4-Flash for routine work. $0.14/$0.28 is unbeatable.
- Escalate to V4-Pro or GLM-5.1 for hard tasks — your choice based on license needs.
- Use Kimi K2.6 specifically when you need parallel sub-agents or guaranteed Apache 2.0.
The real story isn’t which model wins — it’s that the open-weight tier is now within striking distance of Claude Opus 4.7 and GPT-5.5 at one-tenth the cost. Production AI no longer needs to mean US-frontier API spend.
Last verified: April 25, 2026. Sources: DeepSeek V4 release notes, Moonshot AI Kimi K2.6 model card, Z.ai GLM-5.1 model card, Hugging Face leaderboards, AkitaOnRails LLM Coding Benchmark April 2026.