Muse Spark 1.1 vs GPT-5.4
At a Glance
| Compare |  Muse Spark 1.1Meta |  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 | 1,000K | 1,050K |
| Model facts checked | Sep 3, 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 1.1 | GPT-5.4 |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -2.9896% of row best · score · Muse Spark 1.1; 95% CI [-3.52464364, -2.43839720]; sessions 109338; observations 4434474; rank 33 | 1.26100% 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 | 1,464.70100% of row best · rating · muse-spark-1.1; 95% CI [1455.86175356, 1473.53929177]; votes 4885; rank 18 | 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 | 1,480.17100% of row best · rating · muse-spark-1.1; 95% CI [1475.34892554, 1484.99642998]; votes 27615; rank 15 | 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 · statistical tie | 1,294.16100% of row best · rating · muse-spark-1.1; 95% CI [1286.17018729, 1302.15601223]; votes 8447; rank 21 | 1,292.51100% of row best · rating · gpt-5.4; 95% CI [1285.77825721, 1299.23452617]; votes 21188; rank 22 |
| LiveBench2026-06-25 · overall · leader | 78.5795% of row best · percent · muse-spark-1.1-xhigh · 13,652 output tokens / case | 82.38100% of row best · percent · gpt-5.4-xhigh · 18,273 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 80.7694% of row best · points · Muse Spark 1.1 (thinking) · 6,037 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 · 2 ties | 1 benchmark win | 2 benchmark winsOverall lead |
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 1.1 | GPT-5.4 |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Muse Spark | Gpt 5 4 |
| Model | Muse Spark 1.1 | GPT-5.4 |
| Version | Muse Spark 1.1 | GPT-5.4 |
| Lifecycle | preview | active |
| Released | 2026-07-09 | 2026-03-05 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image, Video, Audio | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 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, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
Muse Spark 1.1 Capabilities
GPT-5.4 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.1 vs GPT-5.4 FAQs
Is Muse Spark 1.1 or GPT-5.4 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.1 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 1.1 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 1.1 or GPT-5.4?+
GPT-5.4 has the larger sourced context window. Muse Spark 1.1 supports 1,000K and GPT-5.4 supports 1,050K.
Which performs better in benchmarks, Muse Spark 1.1 or GPT-5.4?+
GPT-5.4 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 1.1 or GPT-5.4 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Muse Spark 1.1 is not marked open weight; GPT-5.4 is not marked open weight.
Can Muse Spark 1.1 and GPT-5.4 understand images?+
Muse Spark 1.1 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 1.1 or GPT-5.4?+
Neither has a larger sourced maximum output. Muse Spark 1.1 is — and GPT-5.4 is 128K.
Do Muse Spark 1.1 and GPT-5.4 support reasoning and tool use?+
Muse Spark 1.1: 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 1.1 or GPT-5.4?+
Muse Spark 1.1 has 0 sourced provider routes; GPT-5.4 has 2, so GPT-5.4 has broader tracked availability.
Which offers better value, Muse Spark 1.1 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.