GLM-5.3 vs GPT-5.4 mini vs Muse Spark 1.2
Muse Spark 1.2 comes out ahead, 72 to 64 and 63 on our weighted score, though GPT-5.4 mini is 16% cheaper per token.
Z.ai (Zhipu)GLM-5.3
64/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
OpenAIGPT-5.4 mini
63/100- ECI148.8
- Price$0.75 / $4.50
- Context400K
- Our pick
MetaMuse Spark 1.2
72/100- ECI155.0
- Price$1.25 / $4.25
- Context1.05M
Muse Spark 1.2 is our pick
Muse Spark 1.2 is the better all-round choice, scoring 72/100 against GLM-5.3 (64) and GPT-5.4 mini (63). It leads on inputs & features. GPT-5.4 mini wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.3Capabilities Index (ECI): GLM-5.3 155.8 · Muse Spark 1.2 155.0 · GPT-5.4 mini 148.8
- Lowest priceGPT-5.4 miniGPT-5.4 mini $1.69 · Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 · GPT-5.4 mini 400,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · GPT-5.4 mini: Text, Images · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | GPT-5.4 mini | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 77 | 84 |
| Price | 25% | 34 | 39 | 36 |
| Inputs & features | 15% | 45 | 70 | 100 |
| Context window | 10% | 60 | 44 | 61 |
| Overall | 100% | 64/100 | 63/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | GPT-5.4 miniOpenAI | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.8 (best) | 148.8 | 155.0 |
| ECI rank | #24 of 148 (best) | #56 of 148 | #30 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% (best) | 86.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% (best) | 51.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% (best) | 88.9% | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 29.4% | 60.3% (best) |
| Price per million tokens | |||
| Input | $1.40 | $0.75 (best) | $1.25 |
| Output | $4.40 | $4.50 | $4.25 (best) |
| Cached input | $0.26 | $0.075 (best) | $0.15 |
| Blended (3:1) | $2.15 | $1.69 (best) | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 400,000 tokens | 1,048,576 tokens (best) |
| Max output | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · high · max | Yeslow · medium · high · xhigh | Yesminimal · low · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | gpt-5.4-mini | muse-spark-1.2 |
| API providers | 62 (best) | 29 | 15 |
| Released | Aug 14, 2026 | Mar 17, 2026 | Aug 5, 2026 |
| Knowledge cutoff | — | Aug 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.3$22.80
- GPT-5.4 mini$16.50
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, GPT-5.4 mini or Muse Spark 1.2?
Muse Spark 1.2 is the better all-round choice, scoring 72/100 against GLM-5.3 (64) and GPT-5.4 mini (63). It leads on inputs & features. GPT-5.4 mini wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, GPT-5.4 mini or Muse Spark 1.2?
GPT-5.4 mini is cheaper at $0.75 input / $4.50 output per million tokens (official OpenAI API price). Muse Spark 1.2 costs $1.25 input / $4.25 output per million tokens (official Meta API price); GLM-5.3 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.69 per million tokens for GPT-5.4 mini versus $2.00 for Muse Spark 1.2 (1.2× as much) and $2.15 for GLM-5.3 (1.3× as much).
Which scores higher on benchmarks?
GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148), Muse Spark 1.2 155.0 (#30 of 148) and GPT-5.4 mini 148.8 (#56 of 148). The confidence ranges of the top two overlap (153.7–158.3 vs 152.8–157.5), so treat the gap as small. On individual benchmarks: SimpleQA Verified — Muse Spark 1.2 60.3%, GLM-5.3 41.0%, GPT-5.4 mini 29.4%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, GPT-5.4 mini and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Muse Spark 1.2 has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3 and 400,000 for GPT-5.4 mini. Maximum output per response: GLM-5.3 up to 131,072, GPT-5.4 mini up to 128,000, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; GPT-5.4 mini accepts text and images; Muse Spark 1.2 accepts text, images, PDFs, audio and video. Muse Spark 1.2 handles the widest range of inputs.
Are any of these open source?
GLM-5.3 publishes its weights and can be self-hosted; GPT-5.4 mini and Muse Spark 1.2 is proprietary.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Muse Spark 1.2 came out Aug 5, 2026; GPT-5.4 mini came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 mini Aug 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
