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GLM-5.3 vs DeepSeek V4.1 Flash
DeepSeek V4.1 Flash 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 DeepSeek V4.1 Flash at 58.7 (out of 100).
Scores as published by each board; table composed November 13, 2026.
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Leaderboard by leaderboard
| Board | GLM-5.3 | DeepSeek V4.1 Flash | Higher |
|---|---|---|---|
| LMArena Elo (community votes) | 1,622.5 | 1,663.7 | DeepSeek V4.1 Flash |
| SimpleBench AVG@5 % (private set) | 66.2 | 66.7 | DeepSeek V4.1 Flash |
| Vals AI Vals Index accuracy % · weighted finance + coding tasks | 59.8 | 61.3 | DeepSeek V4.1 Flash |
| Artificial Analysis intelligence index · measured runs only | 31.7 | 32.6 | DeepSeek V4.1 Flash |
| Epoch AI Epoch Capabilities Index (ECI) · many benchmarks on one scale | 157.1 | 159.4 | DeepSeek V4.1 Flash |
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 DeepSeek V4.1 Flash at 70.4 (#13). DeepSeek V4.1 Flash scores higher on both coding boards that rate them.
| Coding board | GLM-5.3 | DeepSeek V4.1 Flash |
|---|---|---|
| LMArena · Coding | 1,498.5 | 1,503.8 |
| Vals Vibe Code Bench | 78.1 | 87.7 |
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.
DeepSeek V4.1 Flash at a glance
- Developer
- DeepSeek
- Status
- Live since September 10, 2026
Live — Sep 10 · 1M ctx · $0.15/$0.60 per 1M as our sweep read it, which is DeepSeek's off-peak rate (peak hours on weekdays cost double, $0.30/$1.20, per its pricing page) · DeepSeek serves it under the unversioned id deepseek-flash, so the id itself never carries the version · Oct 17: upgraded in place to the 1017 checkpoint (DeepSeek-V4.1-Flash-1017, per DeepSeek's changelog), same deepseek-flash id and price, web and app updated too; open weights on Hugging Face as deepseek-ai/DeepSeek-V4.1-Flash-1017 (MIT, same config.json as the Sep 10 weights); OpenRouter lists it as deepseek/deepseek-v4.1-flash-1017 at $0.15/$0.60, 1M ctx, 384K max output · Oct 19: the Vals Index (updated Oct 18) scores the 1017 build 56.58%, #14 of 50 and the top open-weight model there, up from 51.32% for the Sep 10 build; $0.64 per test on Vals' run, against $0.33 for the Sep 10 build · Oct 19: DHS ordered DeepSeek products, and third-party products built on DeepSeek, excluded from and removed from US federal civilian agencies (Federal Register 2026-21447, publishing Oct 20) · Oct 20: DeepSeek open-sourced DeepInfer (Apache-2.0), the inference engine it serves V4.1 Flash with, for NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs · Oct 20: Vals says the 1017 build completed 16.3 of 21 objectives on its coming offensive-cybersecurity benchmark, against 15.7 for GPT-6 Sol, on several times the tokens (a first finding; the full evaluation is due in the coming weeks).
DeepSeek open-sources DeepInfer, the engine behind V4.1 Flash, built for NVIDIA Blackwell and Huawei Ascend 950
2026-10-20 · DeepSeek released DeepInfer, the inference framework it says serves DeepSeek-V4.1-Flash at scale, as open source on October 20 under the Apache 2.0 license.
Vals: DeepSeek V4.1 Flash beat GPT-6 Sol on its offensive cybersecurity task, using about five times the tokens
2026-10-20 · Vals AI says DeepSeek V4.1 Flash 1017, an open-weight model, completed more objectives than OpenAI's GPT-6 Sol on a multi-step offensive cybersecurity task built on simulated corporate networks.
GLM-5.3 vs DeepSeek V4.1 Flash: quick answers
Which is better, GLM-5.3 or DeepSeek V4.1 Flash?
It depends on what you measure. DeepSeek V4.1 Flash scores higher than GLM-5.3 on all 5 public leaderboards that rate both. DeepSeek V4.1 Flash's widest lead is on LMArena. 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 DeepSeek V4.1 Flash at 58.7 (out of 100).
Which is newer, GLM-5.3 or DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash. It went live on September 10, 2026, 27 days after GLM-5.3 (August 14, 2026).
How do GLM-5.3 and DeepSeek V4.1 Flash compare for coding?
On the ArtificialWatch coding index, GLM-5.3 is at 50.4 (#20 of 48) and DeepSeek V4.1 Flash at 70.4 (#13). DeepSeek V4.1 Flash scores higher on both coding boards that rate them.
Who makes GLM-5.3 and DeepSeek V4.1 Flash?
GLM-5.3 is from Zhipu AI; DeepSeek V4.1 Flash is from DeepSeek.
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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