Muse Spark 1.3 vs GPT-5.4
At a Glance
| Compare |  Muse Spark 1.3Meta |  GPT-5.4OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #14 of 4672.8 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #36 of 44$0.274 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #25 of 3850.1 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.50Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $15.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,050K |
| Model facts checked | Sep 4, 2026View model evidence → | Sep 3, 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 | GPT-5.4 |
|---|---|---|
| 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 | 1.2697% of row best · score · GPT 5.4 (High); 95% CI [0.45972893, 2.06967586]; sessions 80406; observations 3735057; rank 24 |
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,470.66100% of row best · rating · muse-spark-1.3-max; 95% CI [1452.36505006, 1488.94793707]; votes 1006; rank 15 | 1,471.00100% of row best · rating · gpt-5.4; 95% CI [1464.96841782, 1477.04032901]; votes 33331; rank 14 |
| 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 | 1,452.6498% of row best · rating · gpt-5.4; 95% CI [1448.87114809, 1456.41035855]; votes 63526; rank 41 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,314.56100% of row best · rating · muse-spark-1.3-max; 95% CI [1299.74034111, 1329.38210317]; votes 1804; rank 8 | 1,292.5198% of row best · rating · gpt-5.4; 95% CI [1285.77825721, 1299.23452617]; votes 21188; rank 22 |
| LiveBench2026-06-25 · overall · leader | 85.47100% of row best · percent · muse-spark-1.3-xhigh · 28,234 output tokens / case | 82.3896% of row best · percent · gpt-5.4-xhigh · 18,273 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 82.6397% of row best · points · Muse Spark 1.3 (thinking) · 6,301 output tokens / case | 85.60100% of row best · points · GPT-5.4 (xhigh) · 10,941 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 4 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 | GPT-5.4 |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Muse Spark | Gpt 5 4 |
| Model | Muse Spark 1.3 | GPT-5.4 |
| Version | Muse Spark 1.3 | GPT-5.4 |
| Lifecycle | preview | active |
| Released | 2026-09-02 | 2026-03-05 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image, Video | Text, Image |
| Output modalities | Text | Text |
| Context window | Unknown | 1,050K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, computer-use, generation, reasoning, research, tools | chat, generation, reasoning, structured_outputs, tools |
Muse Spark 1.3 Capabilities
GPT-5.4 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.3 vs GPT-5.4 FAQs
Is Muse Spark 1.3 or GPT-5.4 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.3 and GPT-5.4, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Spark 1.3 or GPT-5.4?+
Only GPT-5.4 has a directly sourced input price: $2.50 per million tokens. Only GPT-5.4 has a directly sourced output price: $15.00 per million tokens.
Which has a larger context window, Muse Spark 1.3 or GPT-5.4?+
Neither model has a larger sourced context window in this comparison. Muse Spark 1.3 is — and GPT-5.4 is 1,050K.
Which performs better in benchmarks, Muse Spark 1.3 or GPT-5.4?+
Muse Spark 1.3 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Muse Spark 1.3 or GPT-5.4 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Muse Spark 1.3 is not marked open weight; GPT-5.4 is not marked open weight.
Can Muse Spark 1.3 and GPT-5.4 understand images?+
Muse Spark 1.3 is documented with image input; GPT-5.4 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Muse Spark 1.3 or GPT-5.4?+
Neither has a larger sourced maximum output. Muse Spark 1.3 is — and GPT-5.4 is 128K.
Do Muse Spark 1.3 and GPT-5.4 support reasoning and tool use?+
Muse Spark 1.3: reasoning, tool calling, and image input. GPT-5.4: 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 GPT-5.4?+
Muse Spark 1.3 has 0 sourced provider routes; GPT-5.4 has 2, so GPT-5.4 has broader tracked availability.
Which offers better value, Muse Spark 1.3 or GPT-5.4?+
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.