Compare / head-to-head
GLM-4.6V-FlashXvs
Muse Spark 1.3
GLM-4.6V-FlashX and Muse Spark 1.3 do not yet share a public benchmark, so quality cannot be compared apples-to-apples. GLM-4.6V-FlashX is 12.5x cheaper per token. Muse Spark 1.3 has the larger context window (1.0M tokens).
Benchmarks from cited public sources; pricing from official pages; status from official provider feeds.
Shared benchmarks
0
no overlap yet
Cheaper per token
GLM-4.6V-FlashX
12.5x cheaper, input + output
Larger context
Muse Spark 1.3
1.0M tokens
Providers
2 providers
Z.ai · Meta
GLM-4.6V-FlashX
Z.ai
textvisionvideo
Muse Spark 1.3
Meta · released 2026-09-02
textvisionvideo
Quality
Benchmark matrix
0 shared · 0 only GLM-4.6V-FlashX · 29 only Muse Spark 1.3| Benchmark | GLM-4.6V-FlashX | Muse Spark 1.3 | Δ | Edge |
|---|---|---|---|---|
| Reported by both · 0 | ||||
| No public benchmark is reported for both models yet. | ||||
| Only GLM-4.6V-FlashX reports · 0 | ||||
| GLM-4.6V-FlashX reports nothing Muse Spark 1.3 does not. | ||||
| Only Muse Spark 1.3 reports · 29 | ||||
| LMArena Elo | not reported | 1494.3 ↗ | — | — |
| APEX-Agents (Mercor) | not reported | 57.8% ↗ | — | — |
| AutomationBench | not reported | 49.6% ↗ | — | — |
| Chess Puzzles (Epoch AI run) | not reported | 38% ↗epoch run | — | — |
| DeepSearchQA | not reported | 90.3% ↗ | — | — |
| DeepSWE v1.1 | not reported | 75.4% ↗ | — | — |
| FrontierMath Tier 4 v2 (Epoch AI run) | not reported | 46.3% ↗epoch run | — | — |
| FrontierMath Tiers 1-3 v2 (Epoch AI run) | not reported | 74.4% ↗epoch run | — | — |
| GDPval-AA v2 | not reported | 1754 ↗ | — | — |
| JobBench | not reported | 64.9% ↗ | — | — |
| LiveBench Agentic Coding (LiveBench) | not reported | 64.1% ↗ | — | — |
| LiveBench Coding (LiveBench) | not reported | 81.1% ↗ | — | — |
| LiveBench Data Analysis (LiveBench) | not reported | 79.6% ↗ | — | — |
| LiveBench Instruction Following (LiveBench) | not reported | 78% ↗ | — | — |
| LiveBench Language (LiveBench) | not reported | 82.8% ↗ | — | — |
| LiveBench Mathematics (LiveBench) | not reported | 95.9% ↗ | — | — |
| LiveBench Reasoning (LiveBench) | not reported | 89.7% ↗ | — | — |
| LMArena Agent (LMArena) | not reported | 0.04 ↗ | — | — |
| LMArena Vision (LMArena) | not reported | 1290.2 ↗ | — | — |
| LMArena WebDev (LMArena) | not reported | 1656.6 ↗ | — | — |
| MRCR 256K-512K | not reported | 98.5% ↗ | — | — |
| MRCR 512K-1M | not reported | 98.1% ↗ | — | — |
| Mystery Game Puzzles (Epoch AI run) | not reported | 25% ↗epoch run | — | — |
| OSWorld 2.0 (partial) | not reported | 66.9% ↗ | — | — |
| OTIS Mock AIME 2024-2025 (Epoch AI run) | not reported | 99.2% ↗epoch run | — | — |
| SimpleBench (SimpleBench) | not reported | 81.8% ↗ | — | — |
| SWE-Atlas Codebase QnA | not reported | 59.4% ↗ | — | — |
| Terminal-Bench 2.1 | not reported | 88.8% ↗ | — | — |
| Terminal-Bench 4.0 (Vals AI) | not reported | 24.7% ↗ | — | — |
Scores tagged "epoch" or "matharena" are independent runs, used only where the lab has not published its own; ⚠ marks rows MathArena flags as released after the competition. Higher is better on every row. Δ is GLM-4.6V-FlashX minus Muse Spark 1.3 in the benchmark's own unit. "Not reported" means the lab has not published that figure; it is not a zero. ↗ opens the source.
Specs & pricing
Side by side
Official pricing pages and model cards| Spec | GLM-4.6V-FlashX | Muse Spark 1.3 | Edge |
|---|---|---|---|
| Context window | 128K tokens | 1.0M tokens | Muse Spark 1.3 |
| Max output | 32K tokens | — | — |
| Input price / 1M | $0.04 ↗ | $1.25 ↗ | GLM-4.6V-FlashX |
| Output price / 1M | $0.4 ↗ | $4.25 ↗ | GLM-4.6V-FlashX |
| Cached input / 1M | $0.004 ↗ | $0.15 ↗ | GLM-4.6V-FlashX |
| Input + output / 1M Lower is cheaper. List prices; batch, tool and regional fees excluded. | $0.44 | $5.5 | GLM-4.6V-FlashX |
| Modalities | text · vision · video | text · vision · video | Tie |
| Released | — | 2026-09-02 | — |
| Cited benchmark scores | 0 | 29 | — |
Reliability
Provider status
Live from /statusMore matchups
Muse Spark 1.3 vs …
Models sharing the most benchmarksBuilt by Respan
Which one wins on your data?
Public benchmarks are a starting point. Run GLM-4.6V-FlashX and Muse Spark 1.3 on your own prompts with Respan evals, or route to either through one gateway key with automatic failover.