ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Muse Spark 1.2 vs Gemini 4 Argon vs GLM-5.3

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.

  1. Meta

    Muse Spark 1.2

    Released Aug 5, 2026

    71/100
    • ECI155.2
    • Price$1.25 / $4.25
    • Context1.05M
    Full specs Remove Muse Spark 1.2
  2. Our pick

    Google

    Gemini 4 Argon

    Released Oct 13, 2026New

    74/100
    • ECI166.1
    • Price$2.00 / $10.00
    • Context1.05M
    Full specs Remove Gemini 4 Argon

  3. Z.ai (Zhipu)

    GLM-5.3

    Released Aug 14, 2026

    63/100
    • ECI156.2
    • Price$1.40 / $4.40
    • Context1M
    Full specs Remove GLM-5.3
01 — Verdict

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 contextMuse Spark 1.2 and Gemini 4 ArgonMuse Spark 1.2 1,048,576 · Gemini 4 Argon 1,048,576 · GLM-5.3 1,000,000 tokens
  • Widest inputsMuse Spark 1.2 and Gemini 4 ArgonMuse Spark 1.2: Text, Images, PDFs, Audio, Video · Gemini 4 Argon: Text, Images, PDFs, Audio, Video · GLM-5.3: Text
  • Self-hostingGLM-5.3Publishes downloadable weights
How the score is built
MeasureWeightMuse Spark 1.2Gemini 4 ArgonGLM-5.3
CapabilityCapabilities Index (ECI)50%839684
Price25%342033
Inputs & features15%10010045
Context window10%616160
Overall100%71/10074/10063/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Muse Spark 1.2 vs Gemini 4 Argon vs GLM-5.3 specifications side by side
SpecificationMuse Spark 1.2MetaGemini 4 ArgonGoogleGLM-5.3Z.ai (Zhipu)
Capability
Capabilities Index (ECI)155.2166.1 (best)156.2
ECI rank#38 of 165#7 of 165 (best)#33 of 165
GPQA DiamondGraduate-level science questions—96.4% (best)90.9%
FrontierMath Tiers 1–3Research-level mathematics—86.0% (best)68.8%
OTIS Mock AIME 2024–2025Competition mathematics—100% (best)91.1%
SimpleQA VerifiedShort factual questions60.3%76.0% (best)41.0%
Price per million tokens
Input$1.25 (best)$2.00$1.40
Output$4.25 (best)$10.00$4.40
Cached input$0.15$0.10 (best)$0.26
Blended (3:1)$2.00 (best)$4.00$2.15
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Meta APIOfficial Google APIOfficial Z.AI API
Limits
Context window1,048,576 tokens (best)1,048,576 tokens (best)1,000,000 tokens
Max output131,072 tokens (best)65,536 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsYesYesNo
AudioYesYesNo
VideoYesYesNo
ReasoningYesminimal · low · medium · high · xhighYeslow · medium · highYeslow · high · max
Tool callingYesYesYes
Structured outputYesYesYes
Availability
WeightsProprietaryProprietaryOpen
API model IDmuse-spark-1.2gemini-4-argonglm-5.3
API providers182483 (best)
ReleasedAug 5, 2026Oct 13, 2026Aug 14, 2026
Knowledge cutoff—Mar 2026—
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Muse Spark 1.2$21.00
  • Gemini 4 Argon$40.00
  • GLM-5.3$22.80
04 — Questions

Which should you choose?

Which is better: Muse Spark 1.2, Gemini 4 Argon or GLM-5.3?

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, Muse Spark 1.2, Gemini 4 Argon or GLM-5.3?

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 Muse Spark 1.2, Gemini 4 Argon and GLM-5.3 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?

Muse Spark 1.2 and Gemini 4 Argon 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: Muse Spark 1.2 up to 131,072, Gemini 4 Argon up to 65,536, GLM-5.3 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Muse Spark 1.2 accepts text, images, PDFs, audio and video; Gemini 4 Argon accepts text, images, PDFs, audio and video; GLM-5.3 accepts text. 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; Muse Spark 1.2 and Gemini 4 Argon 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.

05 — Keep comparing

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