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GLM-5.3 vs Claude Opus 5.5
Claude Opus 5.5 scores higher than GLM-5.3 on all 5 public leaderboards that rate both. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, GLM-5.3 is at 46.7 and Claude Opus 5.5 at 90.9 (out of 100). On each maker's API, GLM-5.3 costs $1.40 / $4.40 per 1M tokens (input / output) and Claude Opus 5.5 $4 / $20.
Scores as published by each board; table composed November 13, 2026.
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Leaderboard by leaderboard
| Board | GLM-5.3 | Claude Opus 5.5 | Higher |
|---|---|---|---|
| LMArena Elo (community votes) | 1,622.5 | 1,814.7 | Claude Opus 5.5 |
| SimpleBench AVG@5 % (private set) | 66.2 | 88.4 | Claude Opus 5.5 |
| Vals AI Vals Index accuracy % · weighted finance + coding tasks | 59.8 | 70.1 | Claude Opus 5.5 |
| Artificial Analysis intelligence index · measured runs only | 31.7 | 59.1 | Claude Opus 5.5 |
| Epoch AI Epoch Capabilities Index (ECI) · many benchmarks on one scale | 157.1 | 168.5 | Claude Opus 5.5 |
Raw scores are each board's own scale, so compare within a row, not across rows. Full table: the bench index.
For coding
On the ArtificialWatch coding index, GLM-5.3 is at 50.4 (#20 of 48) and Claude Opus 5.5 at 90 (#3). Claude Opus 5.5 scores higher on both coding boards that rate them.
| Coding board | GLM-5.3 | Claude Opus 5.5 |
|---|---|---|
| LMArena · Coding | 1,498.5 | 1,543.4 |
| Vals Vibe Code Bench | 78.1 | 90.3 |
GLM-5.3 at a glance
- Developer
- Zhipu AI
- Status
- Live since August 14, 2026
- API price
- $1.40 input / $4.40 output per 1M tokens · Z.AI, glm-5.3
- Context window
- 1M tokens
Live - same 743B base as 5.2, every gain from post-training · tops CyberGym · open weights on Hugging Face (zai-org/GLM-5.3, 141 safetensors files, public when we read it Sep 25; repo created Aug 25) · Anthropic research (Sep 29): builds end-to-end exploits near Claude Mythos Preview's rate (ExploitBench 50 of 410 vs 56 of 410), and its safeguards fell to simple bypasses 64-100% of the time · Epoch AI report (Oct 29): built one-click file-reading exploits for all six Firefox releases Sep 17-Oct 26 from published-but-unshipped fixes, about $1,000 each, with no refusals at any provider.
Epoch AI: open-weight GLM 5.3 built working Firefox exploits from security fixes that had not shipped yet, for about $1,000 each
2026-10-30 · An open-weight model can now turn browser security fixes that are public but not yet shipped into working exploits on its own, according to a report Epoch AI published October 29.
Anthropic: open-weights GLM-5.3 nearly matches Claude Mythos Preview at building exploits, and its safeguards are easy to strip
2026-09-29 · Anthropic published research on September 29 testing Z.ai's GLM-5.3, an open-weights model anyone can download, on offensive cyber tasks. Its finding: GLM-5.3 can build working end-to-end exploits at close to the rate of Claude Mythos Preview, the model Anthropic released only to vetted defenders.
Claude Opus 5.5 at a glance
- Developer
- Anthropic
- Status
- Live since September 22, 2026
- API price
- $4 input / $20 output per 1M tokens · Anthropic, claude-opus-5-5
- Context window
- 1M tokens
Live — Sep 22 · $4/$20 per MTok, down from Opus 5 at $5/$25 · 1M ctx / 128K out · adaptive thinking always on, default effort medium · cache reads 5% of base input · Anthropic now says start here for most workloads, and Opus 5 is legacy · fast mode (speed: 'fast', up to 2.5x the output tokens per second by Anthropic's figure) at $8 / $40 per MTok, double standard, left research preview Oct 15 with no waitlist, access request or beta header; on the Claude API and on Claude in Microsoft Foundry (Azure-hosted), not on Bedrock, Google Cloud, Claude Platform on AWS, the Batch API or Priority Tier; @ClaudeDevs says Google Cloud is coming soon (Anthropic's fast mode docs, read Oct 16).
AI models go live a median of 4 months after their knowledge ends: Claude Opus 5.5 knows up to June 2026, Gemini 4 Argon only to March
2026-11-06 · Of the 22 live AI models whose labs publish a knowledge cutoff, the median went live 4 months after its training data ends, by ArtificialWatch's count. The latest published cutoff we found is June 2026, for Anthropic's four current Claude models and OpenAI's GPT-6.1 Terra.
SemiAnalysis launches GDPx: Opus 5.5 leads at 12%, Gemini 4 Carbon close behind
2026-11-02 · SemiAnalysis released GDPx on November 2, a new agentic benchmark of 206 tasks across 11 domains, and the best model it tested scores 12%. Claude Opus 5.5 leads at 12.0, Gemini 4 Carbon is second at 10.9 and GPT-6 Astra third at 9.6. We read the leaderboard directly at gdpx.semianalysis.com.
GLM-5.3 vs Claude Opus 5.5: quick answers
Which is better, GLM-5.3 or Claude Opus 5.5?
It depends on what you measure. Claude Opus 5.5 scores higher than GLM-5.3 on all 5 public leaderboards that rate both. Claude Opus 5.5's widest lead is on SimpleBench. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, GLM-5.3 is at 46.7 and Claude Opus 5.5 at 90.9 (out of 100).
Which is newer, GLM-5.3 or Claude Opus 5.5?
Claude Opus 5.5. It went live on September 22, 2026, 39 days after GLM-5.3 (August 14, 2026).
Which is cheaper, GLM-5.3 or Claude Opus 5.5?
GLM-5.3 is cheaper on both input and output. On each vendor's own API, GLM-5.3 is $1.40 input / $4.40 output per million tokens and Claude Opus 5.5 is $4 input / $20 output per million tokens. Prices as published in the models.dev catalog, as of 2026-11-12.
Which has the bigger context window, GLM-5.3 or Claude Opus 5.5?
Neither. Both take 1M tokens.
How do GLM-5.3 and Claude Opus 5.5 compare for coding?
On the ArtificialWatch coding index, GLM-5.3 is at 50.4 (#20 of 48) and Claude Opus 5.5 at 90 (#3). Claude Opus 5.5 scores higher on both coding boards that rate them.
Who makes GLM-5.3 and Claude Opus 5.5?
GLM-5.3 is from Zhipu AI; Claude Opus 5.5 is from Anthropic.
Where do these numbers come from?
Each score is the leaderboard's own published figure, read by ArtificialWatch (table composed November 13, 2026). Boards measure different things, so raw scores are only comparable within a row.
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