Cheaper overall
GLM 5.3 Flash
49% lower blended rate
Higher benchmark scores
GLM 5.3 Flash
leads on 11 of 11 figures
Bigger context window
GLM 5.3 Flash
1,000,000 vs 400,000 tokens
Cheaper cached input
GPT-5.4 Nano
$0.02 vs $0.03, 33% less
Where each one wins
GLM 5.3 Flash
- Cheaper input tokens $0.15 vs $0.20, 25% less
- Cheaper output tokens $0.50 vs $1.25, 60% less
- Cheaper on all four workloads
- Larger context window 1,000,000 tokens
- Higher overall intelligence (Intelligence Index) 41.9 vs 20.7
- Higher coding ability (Coding Index) 71.5 vs 56.1
- Higher multi-step tool use (Agentic Index) 51.2 vs 16
- Ahead on 8 more benchmark figures
GPT-5.4 Nano
- Cheaper cached input $0.02 vs $0.03, 33% less
- Offers Batch and Flex pricing not listed for GLM 5.3 Flash
What four workloads cost
One run, and the same run a thousand times.
GLM 5.3 Flash is cheaper on all four workloads, by much the same margin each time — about 1.6 times, from $0.0004 against $0.0008 on a chat turn to $0.0615 against $0.0838 on a long document.
| Workload | GLM 5.3 Flash | GPT-5.4 Nano | ×1,000 | Difference |
|---|---|---|---|---|
| Chat turn 1,000 in / 500 out | $0.0004 | $0.0008 | $0.4000 / $0.8250 | GLM 5.3 Flash is 52% cheaper |
| RAG answer 10,000 in / 800 out | $0.0019 | $0.0030 | $1.90 / $3.00 | GLM 5.3 Flash is 37% cheaper |
| Agent loop 32,000 in / 700 out × 20 calls | $0.0310 | $0.0375 | $31.00 / $37.50 | GLM 5.3 Flash is 17% cheaper |
| Long document 400,000 in / 3,000 out | $0.0615 | $0.0838 | $61.50 / $83.75 | GLM 5.3 Flash is 27% cheaper |
Price per 1M tokens
Above 272,000 input tokens the rates change, and the new rate applies to the whole request, not just the tokens past that point. It does not change which of the two is cheaper.
| Context band | Price | GLM 5.3 Flash | GPT-5.4 Nano | Difference |
|---|---|---|---|---|
| Prompts up to 272,000 tokens | Input | $0.15 | $0.20 | GLM 5.3 Flash is 25% cheaper |
| Prompts up to 272,000 tokens | Cached input | $0.03 | $0.02 | GPT-5.4 Nano is 33% cheaper |
| Prompts up to 272,000 tokens | Output | $0.50 | $1.25 | GLM 5.3 Flash is 60% cheaper |
| Prompts over 272,000 tokens | Input | $0.15 | $0.20 | GLM 5.3 Flash is 25% cheaper |
| Prompts over 272,000 tokens | Cached input | $0.03 | $0.02 | GPT-5.4 Nano is 33% cheaper |
| Prompts over 272,000 tokens | Output | $0.50 | $1.25 | GLM 5.3 Flash is 60% cheaper |
Blended: GLM 5.3 Flash $0.2375, GPT-5.4 Nano $0.4625 per 1M — one rate at 3:1 input to output, for comparing two models at a glance.
GLM 5.3 Flash priced by Z.ai, GPT-5.4 Nano by OpenAI. Standard tier, pay-as-you-go.
Other pricing tiers
Only GPT-5.4 Nano lists Batch and Flex, at up to 50% off its own standard rate; GLM 5.3 Flash offers none of them.
Every tier either model sells is here, so this is the whole price sheet. A saving is measured against that model's own standard rate — how we read tiers.
| Tier | GLM 5.3 Flash | GPT-5.4 Nano | Cheaper |
|---|---|---|---|
| Batch only on GPT-5.4 Nano | Not offered | $0.10 in / $0.625 out 50% off Standard | — |
| Flex only on GPT-5.4 Nano | Not offered | $0.10 in / $0.625 out 50% off Standard | — |
Benchmarks
GLM 5.3 Flash leads on all 11 figures, furthest ahead on real-world task performance (GDPval), by 35.9 points.
Overall intelligence
Intelligence Index — Overall intelligence across reasoning, knowledge & math evals
GLM 5.3 Flash ahead by 21.2
Coding ability
Coding Index — Coding ability across software-engineering evals
GLM 5.3 Flash ahead by 15.4
Multi-step tool use
Agentic Index — Tool use & multi-step agent task performance
GLM 5.3 Flash ahead by 35.2
Graduate-level science
GPQA Diamond — Graduate-level questions in biology, chemistry & physics
GLM 5.3 Flash ahead by 9.5
Expert-exam performance
Humanity's Last Exam — Expert-level questions across many academic domains
GLM 5.3 Flash ahead by 11.6
Long-context reasoning
AA-LCR — Long-context reasoning across large inputs
GLM 5.3 Flash ahead by 3.3
Scientific coding
SciCode — Research-level scientific coding tasks
GLM 5.3 Flash ahead by 4.4
Physics research reasoning
CritPt — Unpublished physics research reasoning problems
GLM 5.3 Flash ahead by 6.1
Real-world task performance
GDPval — Economically valuable, real-world knowledge work
GLM 5.3 Flash ahead by 35.9
Factual accuracy
Omniscience Accuracy — Breadth of factual knowledge across domains
GLM 5.3 Flash ahead by 1.8
Answer reliability
Omniscience Non-Hallucination — How reliably the model avoids fabricated answers
GLM 5.3 Flash ahead by 23.5
Each figure is that model's best published run, with the effort level named beside it — where these scores come from.
Specifications
GLM 5.3 Flash holds 600,000 more input tokens in one request. GLM 5.3 Flash also takes video.
| Specification | GLM 5.3 Flash | GPT-5.4 Nano |
|---|---|---|
| Context window | 1,000,000 tokens | 400,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens |
| Takes in | Text, image, video, file | Text, image, file |
| Puts out | Text | Text |
| Knowledge cutoff | — | 31 Aug 2025 |
| Released | 26 Aug 2026 | 17 Mar 2026 |
| Status | Active | Active |
| Sold by | Perplexity, Z.ai | OpenAI, Perplexity |
FAQs
- Is GLM 5.3 Flash cheaper than GPT-5.4 Nano?
- Yes. A 1,000-token prompt with a 500-token reply costs $0.0004 on GLM 5.3 Flash and $0.0008 on GPT-5.4 Nano, and the same model is cheaper on every workload on this page.
- How much do GLM 5.3 Flash and GPT-5.4 Nano cost per 1M tokens?
- GLM 5.3 Flash costs $0.15 for input and $0.50 for output. GPT-5.4 Nano costs $0.20 and $1.25. Standard pay-as-you-go rates, as of 26 Aug 2026.
- Which is better, GLM 5.3 Flash or GPT-5.4 Nano?
- On published benchmarks, GLM 5.3 Flash. It leads on 11 of the 11 figures both models report, including overall intelligence (Intelligence Index), where it scores 41.9 against 20.7.
- Does GLM 5.3 Flash or GPT-5.4 Nano offer batch pricing?
- GPT-5.4 Nano only. Its batch input costs $0.10, 50% off its standard rate, and GLM 5.3 Flash lists no batch tier at all.
- Does GLM 5.3 Flash or GPT-5.4 Nano have a bigger context window?
- GLM 5.3 Flash, at 1,000,000 tokens against 400,000. That is 600,000 more input tokens in a single request.