GLM-5.3 vs MiniMax-M3.1-Flash vs Muse Spark 1.2
MiniMax-M3.1-Flash comes out ahead, 70 to 59 and 42 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.3
42/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
- Our pick
MiniMaxMiniMax-M3.1-Flash
70/100- ECI—
- Price$0.15 / $0.60
- Context1M
MetaMuse Spark 1.2
59/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
MiniMax-M3.1-Flash is our pick
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Muse Spark 1.2 (59) and GLM-5.3 (42). It leads on price. Muse Spark 1.2 wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMiniMax-M3.1-FlashMiniMax-M3.1-Flash $0.263 · 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 · MiniMax-M3.1-Flash 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · MiniMax-M3.1-Flash: Text, Images, Video · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | MiniMax-M3.1-Flash | Muse Spark 1.2 |
|---|---|---|---|---|
| Price | 50% | 33 | 74 | 34 |
| Inputs & features | 30% | 45 | 70 | 100 |
| Context window | 20% | 60 | 60 | 61 |
| Overall | 100% | 42/100 | 70/100 | 59/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | MiniMax-M3.1-FlashMiniMax | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 (best) | — | 155.2 |
| ECI rank | #33 of 165 (best) | — | #38 of 165 |
| GPQA DiamondGraduate-level science questions | 90.9% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | — | — |
| SimpleQA VerifiedShort factual questions | 41.0% | — | 60.3% (best) |
| Price per million tokens | |||
| Input | $1.40 | $0.15 (best) | $1.25 |
| Output | $4.40 | $0.60 (best) | $4.25 |
| Cached input | $0.26 | $0.015 (best) | $0.15 |
| Blended (3:1) | $2.15 | $0.263 (best) | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official MiniMax (minimax.io) API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,000,000 tokens | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 512,000 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · high · max | Yeslow · medium · high · xhigh · max | Yesminimal · low · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | MiniMax-M3.1-Flash | muse-spark-1.2 |
| API providers | 83 (best) | 25 | 18 |
| Released | Aug 14, 2026 | Oct 19, 2026 | Aug 5, 2026 |
| Knowledge cutoff | — | — | — |
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
- MiniMax-M3.1-Flash$2.70
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, MiniMax-M3.1-Flash or Muse Spark 1.2?
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Muse Spark 1.2 (59) and GLM-5.3 (42). It leads on price. Muse Spark 1.2 wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-5.3, MiniMax-M3.1-Flash or Muse Spark 1.2?
MiniMax-M3.1-Flash is cheaper at $0.15 input / $0.60 output per million tokens (official MiniMax (minimax.io) 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 $0.263 per million tokens for MiniMax-M3.1-Flash versus $2.00 for Muse Spark 1.2 (7.6× as much) and $2.15 for GLM-5.3 (8.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5.3 has an ECI of 156.2, MiniMax-M3.1-Flash has not been scored yet and Muse Spark 1.2 has an ECI of 155.2.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, MiniMax-M3.1-Flash and Muse Spark 1.2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 1,000,000 for MiniMax-M3.1-Flash. Maximum output per response: GLM-5.3 up to 131,072, MiniMax-M3.1-Flash up to 512,000, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; MiniMax-M3.1-Flash accepts text, images and video; 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; MiniMax-M3.1-Flash and Muse Spark 1.2 is proprietary.
Which is newer?
MiniMax-M3.1-Flash is the newest, released Oct 19, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 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.
