GLM-5.3 vs Gemini 4 Argon vs Muse Spark 1.2
Gemini 4 Argon comes out ahead, 74 to 71 and 63 on our weighted score, though Muse Spark 1.2 is 2× cheaper per token.
Z.ai (Zhipu)GLM-5.3
63/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
- Our pick
GoogleGemini 4 Argon
74/100- ECI166.1
- Price$2.00 / $10.00
- Context1.05M
MetaMuse Spark 1.2
71/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
Gemini 4 Argon is our pick
Gemini 4 Argon is the better all-round choice, scoring 74/100 against Muse Spark 1.2 (71) and GLM-5.3 (63). It leads on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 4 ArgonCapabilities Index (ECI): Gemini 4 Argon 166.1 · GLM-5.3 156.2 · Muse Spark 1.2 155.2
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Gemini 4 Argon $4.00 per 1M tokens (3:1 blend)
- Longest contextGemini 4 Argon and Muse Spark 1.2Gemini 4 Argon 1,048,576 · Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsGemini 4 Argon and Muse Spark 1.2GLM-5.3: Text · Gemini 4 Argon: Text, Images, PDFs, Audio, Video · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | Gemini 4 Argon | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 96 | 83 |
| Price | 25% | 33 | 20 | 34 |
| Inputs & features | 15% | 45 | 100 | 100 |
| Context window | 10% | 60 | 61 | 61 |
| Overall | 100% | 63/100 | 74/100 | 71/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Gemini 4 ArgonGoogle | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 | 166.1 (best) | 155.2 |
| ECI rank | #33 of 165 | #7 of 165 (best) | #38 of 165 |
| GPQA DiamondGraduate-level science questions | 90.9% | 96.4% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | 86.0% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 100% (best) | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 76.0% (best) | 60.3% |
| Price per million tokens | |||
| Input | $1.40 | $2.00 | $1.25 (best) |
| Output | $4.40 | $10.00 | $4.25 (best) |
| Cached input | $0.26 | $0.10 (best) | $0.15 |
| Blended (3:1) | $2.15 | $4.00 | $2.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Google API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 131,072 tokens (best) | 65,536 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | Yes | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · high · max | Yeslow · medium · high | 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 | gemini-4-argon | muse-spark-1.2 |
| API providers | 83 (best) | 24 | 18 |
| Released | Aug 14, 2026 | Oct 13, 2026 | Aug 5, 2026 |
| Knowledge cutoff | — | Mar 2026 | — |
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
- Gemini 4 Argon$40.00
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, Gemini 4 Argon or Muse Spark 1.2?
Gemini 4 Argon is the better all-round choice, scoring 74/100 against Muse Spark 1.2 (71) and GLM-5.3 (63). It leads on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, Gemini 4 Argon or Muse Spark 1.2?
Muse Spark 1.2 is cheaper at $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); Gemini 4 Argon costs $2.00 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $2.00 per million tokens for Muse Spark 1.2 versus $2.15 for GLM-5.3 (1.1× as much) and $4.00 for Gemini 4 Argon (2× as much).
Which scores higher on benchmarks?
Gemini 4 Argon scores higher on the Capabilities Index (ECI): Gemini 4 Argon 166.1 (#7 of 165), GLM-5.3 156.2 (#33 of 165) and Muse Spark 1.2 155.2 (#38 of 165). Their confidence ranges do not overlap (163.1–170.3 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: SimpleQA Verified — Gemini 4 Argon 76.0%, Muse Spark 1.2 60.3%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, Gemini 4 Argon and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, Gemini 4 Argon 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?
Gemini 4 Argon and Muse Spark 1.2 have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: GLM-5.3 up to 131,072, Gemini 4 Argon up to 65,536, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Gemini 4 Argon accepts text, images, PDFs, audio and video; Muse Spark 1.2 accepts text, images, PDFs, audio and video. Gemini 4 Argon handles the widest range of inputs.
Are any of these open source?
GLM-5.3 publishes its weights and can be self-hosted; Gemini 4 Argon and Muse Spark 1.2 is proprietary.
Which is newer?
Gemini 4 Argon is the newest, released Oct 13, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 2026. Knowledge cutoff: Gemini 4 Argon Mar 2026.
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 Nov 2, 2026.
