Claude Fable 5 vs GLM 5.3 Flash
Claude Fable 5 grades higher overall (90.7 against 82.9). GLM 5.3 Flash costs 99% less for the same work.
Claude Fable 5
by Anthropic · #3 of 67 overall
- Context
- 1M
- Input / 1M
- $10.00
- Output / 1M
- $50.00
GLM 5.3 Flash
by Z.ai · #13 of 67 overall
- Context
- 1M
- Input / 1M
- $0.15
- Output / 1M
- $0.50
Better overall
Claude Fable 5
Grade 90.7 vs 82.9
Cheaper
GLM 5.3 Flash
99% less, $0.24 vs $20.00 blended
Larger context
GLM 5.3 Flash
1M vs 1M tokens
Terminal-Bench 2.1
Tie
84.3% vs 84.3%
Head to head
Public benchmarks. Longer is better; each value links to its source.
Claude Fable 5GLM 5.3 Flashv = reported by the model’s maker. — = no public score yet. Arena bars span the range across every model we grade.
Speed through FullsendAI
Measured through FullsendAI, from one location, Oct 15, 2026, n=20 (5 for long runs).
Claude Fable 5GLM 5.3 Flash
Time to first tokenmedian, tick at p90 · lower is faster
Output speed1,500-token replies, tick at p90
First-try success — for Claude Fable 5 and 47/55 for GLM 5.3 Flash. FullsendAI retries busy responses automatically, so a missed first try costs a moment, not an error.
How we measured
Streamed chat completions with default settings, the two models alternating run by run. Time to first token runs from sending the request to the first streamed token, connection included, over short prompts. Output speed is output tokens a second after the first token, on long replies. One location and one afternoon, so treat small differences as noise.
What it costs you
Drag to match your monthly usage.
GLM 5.3 Flash saves you $3,950 a month at this usage, $47,400 a year.
Capabilities
| Capability | Claude Fable 5 | GLM 5.3 Flash |
|---|---|---|
| Reasoning levels through FullsendAI | Yes | Yes |
| Reads images through FullsendAI | Yes | Yes |
| Open weights | — | Yes |
| Tool calling | — | Yes |
| JSON output | — | Yes |
| Tool calls that worked (measured) | — | 10/10 |
| Valid JSON replies (measured) | — | 10/10 |
Our coding quick check (ten small Python functions against hidden tests; not a benchmark): — for Claude Fable 5, 10/10 for GLM 5.3 Flash.
Maker-published benchmarks
As each maker reports them, with their own harnesses and settings: not graded, and not strictly comparable across makers.
Each score links to where its maker published it. “chart” marks a number read from a chart image.
At a glance
| Attribute | Claude Fable 5 | GLM 5.3 Flash |
|---|---|---|
| Made by | Anthropic | Z.ai |
| Availability | Coming soon | Coming soon |
| Overall grade | Excellent · 90.7 | Excellent · 82.9 |
| Overall rank | #3 of 67 | #13 of 67 |
| Input price | $10.00 per 1M | $0.15 per 1M |
| Output price | $50.00 per 1M | $0.50 per 1M |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Reasoning levels | Yes | Yes |
| Reads images | Yes | Yes |
| Model ID | claude-fable-5 | glm-5.3-flash |
Model card: what the makers publish
| Fact | Claude Fable 5 | GLM 5.3 Flash |
|---|---|---|
| Released | — | Aug 26, 2026 |
| Made by | — | Z.ai (zai-org; Zhipu AI) |
| Licence | — | MIT License |
| Commercial use | — | Allowed (MIT License) |
| Open weights | — | Yes |
| Parameters | — | 320B |
| Active parameters | — | 18B |
| Mixture of experts | — | Yes: 288 routed experts + 1 shared, 8 experts per token, first 3 layers dense |
| Architecture | — | Hybrid sparse + linear attention (linear attention for local dependencies, sparse attention with a lightweight indexer for global context; IndexPool compresses 4 indexer keys into 1), Manifold-Constrained Hyper-Connections (mHC), MTP layer, ViT vision encoder; 45 layers. Z.ai: attention compute and KV cache reduced 3.01x and 4.44x vs GLM-5.3. |
| Context window | — | 1,048,576 tokens |
| Max output | — | 128K (Z.ai API) |
| Input | — | Text, Image, Video, File |
| Output | — | Text |
| Reasoning | — | Thinking with reasoning_effort low/high/max (default max if omitted). On Z.ai's API thinking is forced (thinking.type only supports 'enabled'; cannot be disabled). Chat template clear_thinking defaults to false; pass clear_thinking=true for chat. Interleaved and preserved thinking supported. |
| Tool calling | — | Yes |
| JSON output | — | Structured output / JSON mode via response_format {'type':'json_object'} |
| Recommended sampling | — | temperature 1, top_p 0.95, reasoning_effort max; thinking.clear_thinking false; stream + tool_stream for streaming |
| Training data | — | Newly trained base model; 30T-token multimodal pre-training corpus |
| Other features | — | Context caching; visual coding loop (screenshots/recordings to apps), computer use; GLM-5.3-FlashX variant at ~200 tokens/s on Z.ai; MTP layer (speculative decoding) in architecture |
| Runs locally with | — | SGLang, vLLM, TokenSpeed, Transformers, KTransformers, Unsloth |
Sources
- Released: GLM 5.3 Flash
- Made by: GLM 5.3 Flash
- Licence: GLM 5.3 Flash
- Commercial use: GLM 5.3 Flash
- Open weights: GLM 5.3 Flash
- Parameters: GLM 5.3 Flash
- Active parameters: GLM 5.3 Flash
- Mixture of experts: GLM 5.3 Flash
- Architecture: GLM 5.3 Flash
- Context window: GLM 5.3 Flash
- Max output: GLM 5.3 Flash
- Input: GLM 5.3 Flash
- Output: GLM 5.3 Flash
- Reasoning: GLM 5.3 Flash
- Tool calling: GLM 5.3 Flash
- JSON output: GLM 5.3 Flash
- Recommended sampling: GLM 5.3 Flash
- Training data: GLM 5.3 Flash
- Other features: GLM 5.3 Flash
- Runs locally with: GLM 5.3 Flash
Try both with one key
Same endpoint, same key, both hosted in the USA. Change one word to switch models, in Claude Code, Cursor, Cline or your own code.
Get your API key# Ask either model the same thing curl https://fullsendai.com/v1/chat/completions \ -H "Authorization: Bearer $FULLSEND_API_KEY" \ -d '{"model": "claude-fable-5", "messages": [...]}' # …then swap one word -d '{"model": "glm-5.3-flash", "messages": [...]}'
Questions
Is Claude Fable 5 or GLM 5.3 Flash better?
Claude Fable 5 grades 90.7 against 82.9 for GLM 5.3 Flash, from public benchmarks.
Which is cheaper, Claude Fable 5 or GLM 5.3 Flash?
GLM 5.3 Flash: $0.15 in and $0.50 out per million tokens, against $10.00 and $50.00 through FullsendAI.
Which has the larger context window?
GLM 5.3 Flash, with 1,048,576 tokens.
Can I use both with one API key?
Yes, once they launch: both are coming soon, at the prices shown, and both will use the same FullsendAI key.
Compare with others
Scores: LMArena (CC BY 4.0), SWE-bench, Scale AI SWE-Bench Pro, Terminal-Bench, and model makers’ published results where marked v. Prices are FullsendAI’s, per million tokens. Data as of Oct 28, 2026.