# GLM 5.2 vs Its Competitors — Full Review (July 2026) ## GLM 5.2 Overview | Spec | Details | |---|---| | **Developer** | Z.ai (Zhipu AI), Beijing | | **Release Date** | June 16, 2026 | | **Architecture** | ~753B total / **40B active** MoE | | **License** | **MIT** (open weights on HuggingFace) | | **Context Window** | **1M tokens** | | **Max Output** | 131K tokens | | **Modality** | Text only (no vision) | | **API Price (Input)** | **$1.40 / 1M tokens** | | **API Price (Output)** | **$4.40 / 1M tokens** | | **Cached Input** | ~$0.26 / 1M tokens | | **Thinking Modes** | High / Max | --- ## Key Benchmark Scores (GLM 5.2) | Benchmark | GLM 5.2 Score | Context | |---|---|---| | **SWE-bench Pro** | **62.1** | Beats GPT-5.5 (58.6) | | **Terminal-Bench 2.1** | **81.0** | Massive jump from GLM-5.1 (62.0) | | **MCP-Atlas (agentic)** | **77.0** | Near-tie with Claude Opus 4.8 (77.8) | | **AIME 2026** | **99.2** | Elite math reasoning | | **GPQA-Diamond** | **91.2** | Elite science reasoning | | **FrontierSWE (dominance)** | **74.4** | Within 1 pt of Opus 4.8 (75.1) | | **HLE (w/ tools)** | **54.7** | Beats GPT-5.5 (52.2) | | **BenchLM Overall Score** | **90** | Tied #1 among Chinese models with Qwen3.7 Max | | **Intelligence Index v4.1** | **51** | #1 among all open-weight models | --- ## GLM 5.2 vs Closed Frontier Models ### vs Claude Opus 4.8 (Anthropic) | Dimension | GLM 5.2 | Claude Opus 4.8 | Winner | |---|---|---|---| | **Input Price** | $1.40/M | $5.00/M | **GLM (3.6x cheaper)** | | **Output Price** | $4.40/M | $25.00/M | **GLM (5.7x cheaper)** | | **Context** | 1M | 1M | Tie | | **License** | MIT Open | Closed | **GLM** | | **Vision** | No | Yes | **Opus** | | **SWE-bench Pro** | **62.1** | n/a | GLM (reported) | | **MCP-Atlas** | 77.0 | **77.8** | Opus (by <1 pt) | | **FrontierSWE** | 74.4 | **75.1** | Opus (by <1 pt) | | **AIME 2026** | **99.2** | 95.7 | **GLM (+3.5)** | | **NL2Repo** | 48.9 | **69.7** | Opus (big gap) | | **SWE-Marathon** | 13.0 | **26.0** | Opus (2x) | | **Tool-Decathlon** | 48.2 | **59.9** | Opus | **Verdict:** Claude Opus 4.8 still holds the benchmark crown overall, especially on long-horizon software engineering tasks. But GLM 5.2 is the first open-weights model to make Opus look expensive — it's within 1 point on several agentic evals and costs **3.6–5.7x less**. Opus leads 16 of 19 benchmarks, but GLM wins on math and one terminal-agent harness, and the price + openness gap is massive. --- ### vs GPT-5.5 (OpenAI) | Benchmark | GLM 5.2 | GPT-5.5 | Winner | |---|---|---|---| | **SWE-bench Pro** | **62.1** | 58.6 | **GLM** | | **MCP-Atlas** | **77.0** | 75.3 | **GLM** | | **HLE w/ tools** | **54.7** | 52.2 | **GLM** | | **Price** | **$1.40/$4.40** | Higher | **GLM** | | **License** | **MIT Open** | Closed | **GLM** | | **Vision** | No | Yes | GPT-5.5 | **Verdict:** GLM-5.2 beats GPT-5.5 on the key coding and agentic benchmarks (SWE-bench Pro, MCP-Atlas, HLE with tools) at a fraction of the cost. GPT-5.5 benefits from tighter OpenAI ecosystem integration but GLM is arguably ahead on raw coding capability. --- ### vs Claude Fable 5 (Anthropic) | Dimension | GLM 5.2 | Claude Fable 5 | Winner | |---|---|---|---| | **BenchLM Score** | 90 | **95** | Fable 5 | | **Agentic** | 81.0 | **85.2** | Fable 5 | | **Coding** | 62.1 | **85.6** | Fable 5 (big gap) | | **Knowledge** | 67.2 | **74.8** | Fable 5 | | **Price (Input)** | **$1.40/M** | $10.00/M | **GLM (7x cheaper)** | | **Price (Output)** | **$4.40/M** | $50.00/M | **GLM (11.4x cheaper)** | | **Context** | 1M | 1M+ | Near tie | | **License** | **MIT Open** | Closed | **GLM** | **Verdict:** Claude Fable 5 is the stronger model on benchmarks (95 vs 90), especially in coding (85.6 vs 62.1) where SWE-bench Pro is the biggest separator (80% vs 62.1%). However, Fable 5 costs **7–11x more** than GLM 5.2. For teams that need the quality ceiling on the hardest 10-20% of tasks, Fable 5 wins; for everything else, GLM-5.2 offers incredible value. --- ## GLM 5.2 vs Open-Weight Competitors ### vs DeepSeek V4 Pro (DeepSeek) | Spec | GLM 5.2 | DeepSeek V4 Pro | Advantage | |---|---|---|---| | **Total params** | 753B | **1.6T** | DeepSeek | | **Active params** | **40B** | ~200B | **GLM (more efficient)** | | **Context** | **1M** | 128K–200K | **GLM** | | **Max output** | **131K** | Not disclosed | GLM | | **SWE-bench Verified** | TBD | **80.6%** | DeepSeek (proven) | | **SWE-bench Pro** | **62.1** | 55.4% | **GLM** | | **Intelligence Index** | **51 (#1 open)** | 44 | **GLM** | | **Input Price** | $1.40/M | **$0.27–$0.55/M** | DeepSeek | | **Output Price** | $4.40/M | **$1.10–$2.19/M** | DeepSeek | | **Vision** | No | No | Tie | | **Ecosystem** | Newer | **More mature** | DeepSeek | **Verdict:** GLM-5.2 edges ahead on intelligence benchmarks, context window, and SWE-bench Pro. DeepSeek V4 Pro still wins on raw cost-per-task (2-4x cheaper per token), proven SWE-bench Verified scores, ecosystem maturity, and has more raw parameter headroom. This is the closest matchup — many teams route between both depending on workload. ### vs Kimi K2.7 Code (Moonshot AI) | Spec | GLM 5.2 | Kimi K2.7 Code | Advantage | |---|---|---|---| | **Total params** | 753B | **1T** | Kimi | | **Active params** | 40B | **~30B** | Kimi (efficiency) | | **Context** | **1M** | 256K | **GLM** | | **Vision** | No | **Yes (MoonViT)** | **Kimi** | | **MCP Atlas** | n/a | **76.0** | Kimi | | **MCP Mark Verified** | n/a | **81.1** | Kimi | | **OpenRouter input** | $1.40/M | ~$0.95/M | Kimi | | **Self-host VRAM** | ~half DeepSeek | **~595GB weights** | GLM | | **First-party workspace** | No | **Kimi Code (cursor-style)** | **Kimi** | **Verdict:** Kimi K2.7 Code is the best choice for MCP-tool-heavy agentic stacks and vision-required coding. GLM-5.2 wins on context window size and self-host cost efficiency. For teams building MCP-heavy agent pipelines, Kimi is the open-weight leader. --- ## GLM 5.2 vs Qwen3.7 Max (Alibaba) On the BenchLM Chinese models leaderboard (July 2, 2026), both score **90** (tied #1). Qwen3.7 Max is closed/proprietary with a 1M context window, while GLM-5.2 is open-weight (MIT). Qwen3.7 Max is Alibaba's strongest generalist; GLM-5.2 is the stronger coding-specific choice. --- ## Summary: Where GLM 5.2 Wins & Loses ### ✅ GLM 5.2 Wins When You Need: 1. **Best open-weight intelligence** — #1 on Intelligence Index v4.1 among all open models (51) 2. **Long-horizon agentic coding** — 1M context + SWE-bench Pro lead over GPT-5.5 3. **Cost efficiency at scale** — 3.6–11x cheaper than Claude models 4. **Self-hosting / air-gapped deployment** — MIT license, runs on fewer GPUs than DeepSeek V4 Pro 5. **Front-end coding** — Ranked #2 on Code Arena WebDev (only behind Claude Fable 5) 6. **Flat-rate pricing** — ~$18/month coding plan available ### ❌ GLM 5.2 Loses When You Need: 1. **Vision input** — No multimodal support (vs Claude Opus 4.8, Kimi K2.7 Code) 2. **Absolute quality ceiling** — Claude Fable 5 and Opus 4.8 still ahead on hardest tasks 3. **Cheapest per-token pricing** — DeepSeek V4 Pro is 2-4x cheaper per token 4. **Proven long benchmarks** — Some scores are self-reported; lacks third-party replication on all tests 5. **MCP-heavy agentic workflows** — Kimi K2.7 Code leads there 6. **Ecosystem maturity** — Newer than DeepSeek V4; fewer community fine-tunes ### Bottom Line (June 2026) GLM-5.2 is the **first open-weights model to truly compete with closed frontier models** (GPT-5.5, Claude Opus 4.8) on coding and agentic benchmarks while costing a fraction of the price. It's the best open-weight coding model for long-horizon tasks, tied for #1 among Chinese models (BenchLM score 90), and the highest open-weight model on the Intelligence Index. The smartest deployment strategy for most teams in mid-2026: keep Claude Fable 5 or Opus 4.8 for the hardest 10-20% of tasks, and route 60-80% of agentic coding to GLM-5.2 (and/or DeepSeek V4 Pro / Kimi K2.7 Code depending on workload type).