text model comparison
GPT-6.1 Terra vs Muse Spark 1.3
GPT-6.1 Terra and Muse Spark 1.3 are both current text models, from OpenAI and Meta. GPT-6.1 Terra is 20% cheaper per 1M input tokens than Muse Spark 1.3, $1 vs $1.25. Both accept about 1.05M tokens of context (1,050,000 and 1,048,576).
- Lower price per 1M input tokens
- GPT-6.1 Terra$1 against $1.25
- Longer context window
- GPT-6.1 Terra1.05M tokens against 1.05M tokens
- Newer release
- GPT-6.1 TerraOct 2026 against Sep 2026
Data verified October 28, 2026 · Dataset 2026-10-28
Side by side
GPT-6.1 Terra and Muse Spark 1.3, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | GPT-6.1 Terra | Muse Spark 1.3 |
|---|---|---|
| Price | ||
| Price per 1M input tokens | $1 (better) | $1.25 |
| Price per 1M output tokens | $5 | $4.25 (better) |
| Independent benchmarks | ||
| Arena Text | Not measured | 1494 Elo (#6 of 49) |
| FrontierMath Tiers 1-3 (v2) | Not measured | 74.4% (#18 of 40) |
| Specs | ||
| Provider | OpenAI | Meta |
| Release date | October 26, 2026 | September 2, 2026 |
| Access | API only | API only |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Max output | 128,000 tokens | Not reported |
| Input | text, image | text, image, audio, video |
| Output | text | text |
No independent benchmark has scored both models yet. Each rank counts the current models scored on that benchmark.
Charts
Against every current text model
Where GPT-6.1 Terra and Muse Spark 1.3 land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
Only Muse Spark 1.3 has a current Arena Text score: #6 of 49. Each bar is the share of current models it beats.
Independent benchmarks
Arena Text
GPT-6.1 Terra: Not scored
Muse Spark 1.3:#6of 49
1,494 Elo · ahead of 90%
FrontierMath Tiers 1-3 (v2)
GPT-6.1 Terra: Not scored
Muse Spark 1.3:#18of 40
74.4% · ahead of 56%
Price and context
Price per 1M input tokens
GPT-6.1 Terra:#19of 51
$1 · cheaper than 62%
Muse Spark 1.3:#21of 51
$1.25 · cheaper than 56%
Price per 1M output tokens
GPT-6.1 Terra:#26of 51
$5 · cheaper than 50%
Muse Spark 1.3:#24of 51
$4.25 · cheaper than 54%
Context window
GPT-6.1 Terra:#1of 66
1.05M tokens · longer than 86%
Muse Spark 1.3:#11of 66
1.05M tokens · longer than 65%
Quality against price
Muse Spark 1.3 scores 1,494 at $2 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.
Quality against price: the data
| Model | Provider | Blended price per 1M tokens | Arena Text (Elo) |
|---|---|---|---|
| Gemini 4 Argon | $4 | 1,525 | |
| Claude Opus 5.5 | Anthropic | $8 | 1,507 |
| Claude Fable 5 | Anthropic | $20 | 1,504 |
| Claude Fable 5.1 | Anthropic | $20 | 1,501 |
| Gemini 3.8 Flash | $1.50 | 1,497 | |
| Muse Spark 1.3 | Meta | $2 | 1,494 |
| Claude Opus 5 | Anthropic | $10 | 1,490 |
| Kimi K3 | Moonshot Kimi | $6 | 1,488 |
| Gemini 3.7 Flash | $1.50 | 1,486 | |
| GPT-5.6 Sol | OpenAI | $8 | 1,485 |
| GPT-6.1 Sol | OpenAI | $4 | 1,484 |
| Qwen3.8-Max | Alibaba Qwen | $3 | 1,483 |
| GPT-5.5 | OpenAI | $11 | 1,482 |
| Gemini 3.6 Flash | $3 | 1,482 | |
| Claude Opus 4.8 | Anthropic | $10 | 1,481 |
| MiMo-V2.6-Pro | Xiaomi MiMo | $0.54 | 1,480 |
| GLM-5.3 | Z.ai | $2.15 | 1,478 |
| Claude Sonnet 5.5 | Anthropic | $4 | 1,476 |
| GPT-5.4 | OpenAI | $5.63 | 1,475 |
| GPT-6 Astra | OpenAI | $20 | 1,475 |
| DeepSeek-V4.1-Flash | DeepSeek | $0.52 | 1,475 |
| GLM-5.3-Flash | Z.ai | $0.24 | 1,475 |
| Gemini 3 Flash Preview | $1.13 | 1,473 | |
| Grok 4.20 | xAI | $1.56 | 1,472 |
| GPT-5.6 Terra | OpenAI | $4.50 | 1,466 |
| Grok 4.5 | xAI | $3 | 1,466 |
| DeepSeek-V4-Pro | DeepSeek | $1.98 | 1,465 |
| Claude Sonnet 5 | Anthropic | $4 | 1,461 |
| GPT-6 Sol | OpenAI | $4 | 1,456 |
| Gemini 3.5 Flash-Lite | $0.85 | 1,455 | |
| Grok 4.6 | xAI | $3 | 1,454 |
| GPT-5.6 Luna | OpenAI | $0.45 | 1,452 |
| MiMo-V2.6-Flash | Xiaomi MiMo | $0.18 | 1,451 |
| GPT-6 Luna | OpenAI | $0.20 | 1,443 |
| Grok 4.7 | xAI | $3 | 1,443 |
| Grok 4.3 | xAI | $1.56 | 1,442 |
| Mistral Large 4 | Mistral AI | $2.06 | 1,429 |
| GPT-4.1 | OpenAI | $3.50 | 1,415 |
| Solar Pro 4 | Upstage | $0.52 | 1,386 |
| GPT-4.1 mini | OpenAI | $0.70 | 1,383 |
| GPT-4.1 nano | OpenAI | $0.18 | 1,322 |
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
The same work, priced
For 10M input and 2M output tokens, Muse Spark 1.3 costs 1.1 times as much as GPT-6.1 Terra, at each provider's listed rates.
10M input and 2M output tokens
Muse Spark 1.3 costs 1.1× as much
- GPT-6.1 Terra
- $20
- Muse Spark 1.3
- $21
The same, with 9M of the input tokens cached
GPT-6.1 Terra costs 1.0× as much
- GPT-6.1 Terra
- $11.45
- Muse Spark 1.3
- $11.10
| Workload or rate | GPT-6.1 Terra | Muse Spark 1.3 |
|---|---|---|
| Example workloads | ||
| 10M input and 2M output tokens | $20 (better) | $21 |
| The same, with 9M of the input tokens cached | $11.45 | $11.10 (better) |
| Other listed rates | ||
| 1M input tokens, cached | $0.05 (better) | $0.15 |
| 1M input tokens, prompts over 272K tokens | $2 | Not listed |
| 1M output tokens, prompts over 272K tokens | $7.50 | Not listed |
Verdict
When to choose each
GPT-6.1 Terra
Where it leads
- Lower price per 1M input tokens, $1 vs $1.25
- Lower cost for 10M input and 2M output tokens, $20 vs $21
- Cheaper cached input, $0.05 vs $0.15 per 1M tokens
Best for
- Computer-use and tool-calling agents at half GPT-6.1 Sol's price
- High-volume reasoning work that stays under the 272K long-context threshold
Watch out for
- Prompts over 272,000 input tokens bill the whole request at $2 input and $7.50 output per 1M tokens
- Reads text and images only: audio and video input are not supported
- Released on 26 October 2026, so no independent benchmark score is recorded here yet
Muse Spark 1.3
Where it leads
- Lower price per 1M output tokens, $4.25 vs $5
- Accepts audio and video input
Best for
- Tool-heavy agent loops where fewer calls per task matters as much as the per-token rate
- Coding agents at volume, where its coding scores and low price line up
Watch out for
- Meta’s own documentation states audio understanding is not fully supported and response quality may be degraded on requests containing audio
- The much cheaper contributor endpoint (about $0.10 / $0.20 per million) is priced that way in exchange for Meta training on your traffic. The standard tier is the rate recorded here.
- Meta’s scorecard has it trailing Claude Opus 5 on every broader agent-workflow benchmark it published, including GDPVal-AA v2, despite winning the coding rows
From benchmark to production
Building on GPT-6.1 Terra or Muse Spark 1.3?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
FAQ
GPT-6.1 Terra vs Muse Spark 1.3 FAQ
Which is cheaper, GPT-6.1 Terra or Muse Spark 1.3?
GPT-6.1 Terra is 20% cheaper per 1M input tokens than Muse Spark 1.3, $1 vs $1.25. For 10M input and 2M output tokens, GPT-6.1 Terra costs $20 and Muse Spark 1.3 $21 at standard rates.
Which is cheaper with prompt caching, GPT-6.1 Terra or Muse Spark 1.3?
GPT-6.1 Terra charges $0.05 per 1M cached input tokens, 95% below its standard input rate, and Muse Spark 1.3 $0.15, 88% below. With 9M of 10M input tokens cached and 2M output tokens, GPT-6.1 Terra costs $11.45 and Muse Spark 1.3 $11.10.
Which has the larger context window, GPT-6.1 Terra or Muse Spark 1.3?
Both accept about 1.05M tokens of context (1,050,000 and 1,048,576).
How do GPT-6.1 Terra and Muse Spark 1.3 differ in features?
GPT-6.1 Terra takes text and image input; Muse Spark 1.3 takes text, image, audio and video.
Further reading
More on GPT-6.1 Terra and Muse Spark 1.3
Articles on GPT-6.1 Terra and Muse Spark 1.3
- GPT-6.1 Terra: Price, Benchmarks and When to Use ItOctober 27, 2026
Related comparisons
Production evaluation
Choosing between GPT-6.1 Terra and Muse Spark 1.3?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty