Our Grok 4.5 vs Muse Spark 1.1 read crosses the benchmarks published since both models shipped (July 8 for Grok, July 9 for Muse Spark) with the independent tests available so far. The duel does not resolve in a single block: Grok 4.5 leads on raw coding and token efficiency, while Muse Spark 1.1 flips the board on multi-tool orchestration and price.
Key Takeaways
- Grok 4.5 leads SWE-Bench Pro at 64.7% and delivers roughly 14,000 output tokens per hard task, a token efficiency edge that shows up across the Intelligence Index board.
- Muse Spark 1.1 wins on MCP Atlas at 88.1, on JobBench at 54.7, and lands with a bill roughly one third cheaper than Grok 4.5 for the same workload.
- Verdict: Grok 4.5 for high volume coding pipelines, Muse Spark 1.1 for agentic orchestration and long tool chains where computer use is on the table.
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ChatGPTThe protocol behind our comparative read
We framed the comparison around three families of public measurements available at launch. The first covers integrated coding benchmarks, with SWE-Bench Pro as the spine, DeepSWE 1.1 for debugging, and Terminal-Bench 2.1 for longer shell tasks.
The second family looks at agentic and tool orchestration workloads. MCP Atlas measures a model’s ability to chain calls through the Model Context Protocol, the standard Anthropic pushes and Meta picked up natively for Muse Spark 1.1. JobBench scores real professional tool use across tax, healthcare and legal scenarios.
The third family reads the economics. Grok 4.5 lists at 2 dollars per million input tokens and 6 per million output tokens. Muse Spark 1.1 lands at 1.25 and 4.25 on the same axes. On an average task, the Muse Spark bill runs roughly one third under Grok 4.5, with similar output magnitudes. That price gap is what shifts the deployment conversation.
The context floor splits the two models on a very different axis. Grok 4.5 works with 500,000 tokens of context, while Muse Spark 1.1 climbs to one million. On short tasks the gap is zero. On repo scale analysis, long report drafting or multi-agent sessions where history piles up, the extra token budget on the Muse Spark side opens workflows that Grok simply cannot fit in one call.
Where Grok wins, where Muse Spark wins
On SWE-Bench Pro, Grok 4.5 posts 64.7% of tasks solved, against 52.4% for Muse Spark 1.1. The 12 point gap is real, and it does not read as a finetune tweak: it reflects the training pipeline co-built with Cursor, with real developer sessions injected during pretraining. Grok 4.5 is the first xAI model built explicitly for coding, and the difference lands on tasks that demand precise stack tracing. Our dedicated test already confirmed it, Grok 4.5 holding its coding promise.
Token efficiency is the other Grok 4.5 differentiator. On the Artificial Analysis Intelligence Index workload, the average output settles around 14,000 tokens per task, against north of 67,000 for Claude Opus 4.8. The ratio sits near a factor of five. At equal quality, that saving reshapes the budget window for a team that pipelines many short tasks. We measured the same vector in our Claude Sonnet 5 test after a pro week.
Muse Spark 1.1 flips the table on orchestration. The MCP Atlas score peaks at 88.1, taking the number one position on that benchmark. JobBench, which grades tool use inside concrete professional scenarios, lands at 54.7 for Muse Spark, also top. On Tax, Healthcare and Legal benchmarks, the Meta model dethrones Grok 4.5 across all three. The read is clean: Muse Spark is the conductor, Grok 4.5 is the specialist performer.
Multimodality reinforces the Muse Spark edge. The Meta model natively handles text, image, tool use and computer use in one call. Grok 4.5 stays anchored on coding and reasoning over text, with multimodal extensions that feel less mature. For a product team building an agent that has to read a screen, click, type and chain steps, Muse Spark 1.1 arrives with a broader stack out of the box.
Price completes the picture with a structural gap. Muse Spark 1.1 runs roughly one third under Grok 4.5 per average task, and about one quarter of Anthropic and OpenAI flagship pricing. The Meta positioning echoes the one already used for Muse Spark 1.0, and mirrors the price move we documented in our piece on Grok 4.5 undercutting Claude Opus at a third of the price. Price is now a decision variable as heavy as raw quality.
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Team by team verdict
For a dev team pipelining large scale refactors, unit test generation or Python migrations at volume, our cross-read leans toward Grok 4.5. The SWE-Bench Pro number and the token efficiency profile combine into an unbeatable ratio once you pass a few thousand tasks a month. The native Cursor tie-in makes IDE integration nearly transparent.
For a product team building a business assistant agent, with several tools chained (search, API calls, PDF extraction, UI interaction), the verdict flips to Muse Spark 1.1. The MCP Atlas, JobBench and native multimodality trio covers use cases that Grok 4.5 has to backfill with external orchestration layers, and that backfill costs delivery time on the integration side.
For a research or analysis team working on long reports, multi source synthesis or multi-agent sessions where history balloons, the one million token context on Muse Spark 1.1 shifts the game. Grok 4.5 holds 500,000 tokens, which is still generous, but the Meta headroom opens workflows that were priced out of context on prior frontier stacks.
The last axis is pure budget. A CFO comparing the two offers will see Muse Spark 1.1 come out ahead on the monthly bill, with roughly one third saved at equivalent volume. Grok 4.5 stays competitive against Anthropic and OpenAI flagships, but loses the price duel head to head with the Meta rival. On projects with heavy inference volume, the price variable weighs quickly heavier than a few benchmark points.
Our synthesis carries an important caveat. The benchmarks published five days after the Muse Spark 1.1 launch are still moving, and the next wave of independent tests over the coming two weeks may shift the cursor. On SWE-Bench Pro specifically, Grok 4.5 was tuned on Cursor data close to the benchmark structure, which leaves the out of distribution question open. We will track those updates.
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