Muse Spark 1.3 vs GLM 5.3 Flash
At a Glance
| Compare |  Muse Spark 1.3Meta |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Sep 4, 2026View model evidence → | Sep 2, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Muse Spark 1.3 | GLM-5.3-Flash |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | 4.20100% of row best · score · Muse Spark 1.3 (Max); 95% CI [3.33950614, 5.06185896]; sessions 31052; observations 2525117; rank 15 | 1.1597% of row best · score · GLM 5.3 Flash; 95% CI [0.48275443, 1.81278415]; sessions 43164; observations 4433017; rank 25 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,489.74100% of row best · rating · muse-spark-1.3-max; 95% CI [1480.91456937, 1498.56660928]; votes 4723; rank 10 | 1,471.8999% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,314.56100% of row best · rating · muse-spark-1.3-max; 95% CI [1299.74034111, 1329.38210317]; votes 1804; rank 8 | 1,298.7199% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17 |
| LiveBench2026-06-25 · overall · leader | 85.47100% of row best · percent · muse-spark-1.3-xhigh · 28,234 output tokens / case | 73.2786% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 82.6394% of row best · points · Muse Spark 1.3 (thinking) · 6,301 output tokens / case | 88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 3 benchmark winsOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Muse Spark 1.3 | GLM-5.3-Flash |
|---|---|---|
| Developer | Meta | Z.ai |
| Family | Muse Spark | Glm 5 3 Flash |
| Model | Muse Spark 1.3 | GLM-5.3-Flash |
| Version | Muse Spark 1.3 | GLM-5.3-Flash |
| Lifecycle | preview | active |
| Released | 2026-09-02 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | Unknown | 1,000K |
| Total parameters | Unknown | 320B |
| Active parameters | Unknown | 18B |
| License | Unknown | MIT |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, computer-use, generation, reasoning, research, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
Muse Spark 1.3 Capabilities
GLM 5.3 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.3 vs GLM 5.3 Flash FAQs
Is Muse Spark 1.3 or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.3 and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Spark 1.3 or GLM 5.3 Flash?+
Only GLM 5.3 Flash has a directly sourced input price: $0.075 per million tokens. Only GLM 5.3 Flash has a directly sourced output price: $0.25 per million tokens.
Which has a larger context window, Muse Spark 1.3 or GLM 5.3 Flash?+
Neither model has a larger sourced context window in this comparison. Muse Spark 1.3 is — and GLM 5.3 Flash is 1,000K.
Which performs better in benchmarks, Muse Spark 1.3 or GLM 5.3 Flash?+
Muse Spark 1.3 leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Muse Spark 1.3 or GLM 5.3 Flash be self-hosted?+
GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. Muse Spark 1.3 is not marked open weight; GLM 5.3 Flash is open weight.
Can Muse Spark 1.3 and GLM 5.3 Flash understand images?+
Muse Spark 1.3 is documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Muse Spark 1.3 or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. Muse Spark 1.3 is — and GLM 5.3 Flash is 131K.
Do Muse Spark 1.3 and GLM 5.3 Flash support reasoning and tool use?+
Muse Spark 1.3: reasoning, tool calling, and image input. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Muse Spark 1.3 or GLM 5.3 Flash?+
Muse Spark 1.3 has 0 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, Muse Spark 1.3 or GLM 5.3 Flash?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.