Core Conclusion
June AI’s Models 2026 Ultimate Lineup reveals a historic shift: the open model camp’s scale and comprehensive capability now directly compete with closed-source flagships. This isn’t “budget vs premium” — it’s two parallel ecosystems in direct competition.
Full Lineup Comparison
Open Camp (Open Weights)
| Model | Company | Parameters | Characteristics |
|---|---|---|---|
| GLM 5.1 | Zhipu AI | — | Long-horizon Agent capability |
| DeepSeek V4 Pro | DeepSeek | ~1.5T (MoE) | Coding/reasoning surpasses closed flagships |
| DeepSeek V4 Flash | DeepSeek | — | Optimized for high-throughput scenarios |
| Kimi K2.6 | Moonshot AI | — | Coding-driven, autonomous execution, Swarm orchestration |
| Qwen3.5 397B | Alibaba | 397B | #1 open intelligence index |
| Gemma 4 31B | 31B | Lightweight, local inference friendly |
Closed Camp (Proprietary)
| Model | Company | Characteristics |
|---|---|---|
| GPT 5.5 | OpenAI | Fresh base model, 1.5T params, super app strategy |
| Grok 4.1 Fast | xAI | Real-time information processing, fast inference |
| Claude Opus 4.7 | Anthropic | Creative/safety/constitutional AI |
| Gemini 3.1 Pro | Multimodal, long context |
Landscape Analysis
Open vs Closed: Number Comparison
Open camp: 6 models
Closed camp: 4 modelsIn 2024, this ratio was 2:8. By May 2026, it’s 6:4. Open models have gone from “marginal supplement” to “primary choice.”
Internal Dynamics of the Open Camp
Chinese Models Dominate Open Source
Of the 6 open models, 4 are from Chinese companies:
- GLM 5.1 (Zhipu)
- DeepSeek V4 Pro/Flash (DeepSeek)
- Kimi K2.6 (Moonshot AI)
- Qwen3.5 397B (Alibaba)
This is a structural shift. Chinese open models are defining global open AI standards.
Differentiated Positioning
| Scenario | Recommended Model | Reason |
|---|---|---|
| Code generation/Agent | DeepSeek V4 Pro | SWE-bench 92.3%, price $0.14/M tokens |
| Long-horizon autonomous execution | Kimi K2.6 | Swarm orchestration, sustained autonomous execution |
| General intelligence | Qwen3.5 397B | #1 open intelligence index, strongest comprehensive capability |
| Long-horizon Agent tasks | GLM 5.1 | Zhipu’s deep optimization for Agent scenarios |
| Local deployment/edge | Gemma 4 31B | 31B params runnable on consumer GPUs |
| High-throughput processing | DeepSeek V4 Flash | Extremely cost-effective batch processing |
How the Closed Camp Responds
Closed models still maintain advantages in:
- Multimodal capability: Gemini 3.1 Pro and Claude Opus 4.7 still lead in image/video understanding
- Safety/compliance: Anthropic’s constitutional AI and GPT 5.5’s enterprise SLA
- Ecosystem integration: OpenAI’s Codex + ChatGPT platform integration
- Brand trust: Enterprise customers’ trust in closed-source vendors remains higher
But the gap is closing. DeepSeek V4 Pro has already surpassed Opus 4.7 and GPT-5.5 Medium in coding and reasoning.
Practical Impact for Developers
Selection Strategy: Not Either/Or, but Combination
The best practice in 2026 isn’t “pick one model and stick with it” — it’s choosing the right model for each scenario:
Daily coding → DeepSeek V4 Pro (cheap and strong)
Complex reasoning → Qwen3.5 397B or DeepSeek V4 Pro
Agent orchestration → Kimi K2.6 (native Swarm support)
Creative writing → Claude Opus 4.7 (still has the edge)
Multimodal tasks → Gemini 3.1 Pro
Local inference → Gemma 4 31BCost Optimization Example
For an AI app processing 100M tokens daily:
| Strategy | Daily Cost | Monthly Cost |
|---|---|---|
| All GPT-5.5 | $1,000 | $30,000 |
| All Opus 4.7 | $1,500 | $45,000 |
| 70% V4 Pro + 30% closed | $300 + $450 = $750 | $22,500 |
| 90% V4 Pro + 10% closed | $140 + $150 = $290 | $8,700 |
Model Routing strategy can save 70-90% in costs.
Landscape Assessment
June AI’s Models 2026 lineup sends several key signals:
- Open vs closed enters “stalemate phase”: Open is no longer “a worse alternative”
- Chinese models define open standards: Global open AI话语权 is shifting east
- Model selection shifts from “faith” to “engineering”: Choose the right model based on task characteristics
In H2 2026, we may see:
- More open models surpassing closed-source in benchmarks
- Model routing/hybrid usage becoming industry standard
- Closed vendors forced to make bigger concessions on price or capability
Action Recommendations
- If you use only one model: Add at least one open model as a baseline comparison
- If you’re building AI products: Implement model routing, choose the optimal model per scenario
- If you’re making technology decisions: Open models are now the “default option” — closed models need to answer “why pick me”