vs
0:2
overall capability wins (0 ties across 2 scored). Muse Spark 1.1 leads overall.
- Pricing3:0
- Speed0:2
- Specs0:2
Verdict
Muse Spark 1.1 offers ~256.0x larger context (1.0M vs 4K); Muse Spark 1.1 allows ~32.0x more max output (131K vs 4K); Llama 2 70B ships open weights (Llama 2).
- Muse Spark 1.1 offers ~256.0x larger context (1.0M vs 4K)
- Muse Spark 1.1 allows ~32.0x more max output (131K vs 4K)
- Llama 2 70B ships open weights (Llama 2)
- Llama 2 70B is ~2.2x cheaper on a blended token basis than Muse Spark 1.1
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Llama 2 70B
- $1.13
- Muse Spark 1.1
- $2.31
Llama 2 70B is about 2.1x cheaper on this workload.
Price per 1M tokens
Full comparison
Organization
Meta
Meta
Family
Llama / Muse
Llama / Muse
License
Llama 2
Proprietary
Open weights
Yes
No
Release
Jul 18, 2023
Jul 9, 2026
Knowledge cutoff
-
-
API / provider
Together / Fireworks (ref.)
Meta
Modalities
text → text
text, image, video, audio, pdf → text
Specs
Context window
4K
1.0M
Max output
4K
131K
Parameters
70B
—
Pricing
Input $/1M
$0.90
$1.25
Output $/1M
$0.90
$4.25
Blended $/1M (3∶1)
$0.90
$2
Speed
tok/s
40
90
TTFT (s)
0.5
0.4
Reasoning
MMLU-Pro
—
—
GPQA Diamond
—
89.8
Humanity's Last Exam
—
46.2
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
52.1
AA-LCR v1.1
—
77.7
CritPt
—
15.1
MMMU-Pro
—
—
IFBench
—
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
77.9
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
58.8
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
1,542
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
1,493
| Metric | Llama 2 70B | Muse Spark 1.1 | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Meta | Meta | — |
| Family | Llama / Muse | Llama / Muse | — |
| License | Llama 2 | Proprietary | — |
| Open weights | Yes | No | — |
| Release | Jul 18, 2023 | Jul 9, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Together / Fireworks (ref.) | Meta | — |
| Modalities | text → text | text, image, video, audio, pdf → text | — |
| Specs | |||
| Context window | 4K | 1.0M | B +1.0M |
| Max output | 4K | 131K | B +127K |
| Parameters | 70B | — | — |
| Pricing | |||
| Input $/1M | $0.90 | $1.25 | A +$0.35 |
| Output $/1M | $0.90 | $4.25 | A +$3.35 |
| Blended $/1M (3∶1) | $0.90 | $2 | A +$1.10 |
| Speed | |||
| tok/s | 40 | 90 | B +50 |
| TTFT (s) | 0.5 | 0.4 | B +0.10 |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | — | 89.8 | — |
| Humanity's Last Exam | — | 46.2 | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | 52.1 | — |
| AA-LCR v1.1 | — | 77.7 | — |
| CritPt | — | 15.1 | — |
| MMMU-Pro | — | — | — |
| IFBench | — | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | 77.9 | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | 58.8 | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | 1,542 | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | 1,493 | — |
Llama 2 70B
- Context
- 4K / 4K out
- Parameters
- 70B
- Price
- $0.90 / $0.90
- Speed
- 40 tok/s · 0.5s TTFT
- Modalities
- text → text
- License
- Llama 2
Foundational open chat model of 2023 — still cited in fine-tune and RAG legacy stacks.
Muse Spark 1.1
- Context
- 1.0M / 131K out
- Parameters
- —
- Price
- $1.25 / $4.25
- Speed
- 90 tok/s · 0.4s TTFT
- Modalities
- text, image, video, audio, pdf → text
- License
- Proprietary
Meta's first paid API model - agentic coding focus with MCP support and 1M context; no numeric scorecard retained until the report's exact model/protocol values are independently transcribed.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.
These two models have no overlapping published benchmarks in our dataset. Compare specs and pricing instead.