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Model comparison

GPT-5.6 Terra vs Muse Spark 1.2

Updated August 5, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Terra

OpenAI

72.3/100

Estimated · Public rank #12

90% interval 62.5–82.0

Muse Spark 1.2

Meta

60.3/100

Estimated · Public rank #49

90% interval 48.7–71.8

GPT-5.6 Terra has the higher public score estimate, 72.28 versus 60.25, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Terra

    GPT-5.6 Terra has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
GPT-5.6 Terra only
21
Muse Spark 1.2 only
2
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
GPT-5.6 Terra
87.4
Muse Spark 1.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Terra
63.4
Muse Spark 1.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Muse Spark 1.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Terra
92.9
Muse Spark 1.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Terra
80.8
Muse Spark 1.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Muse Spark 1.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-5.6 Terra
$0.008
Fits in one request
Muse Spark 1.2
$0.00338
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Terra
$0.136
Fits in one request
Muse Spark 1.2
$0.07525
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.6 Terra
$0.2
Fits in one request
Muse Spark 1.2
$0.0975
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Muse Spark 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Muse Spark 1.2

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Muse Spark 1.2

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Muse Spark 1.2

Proprietary

License

GPT-5.6 Terra

Proprietary

Muse Spark 1.2

Proprietary

Release date

GPT-5.6 Terra

2026-07-09

Muse Spark 1.2

2026-08-05

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
GPT-5.6 Terra has the higher public score estimate, 72.28 versus 60.25, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.07525. Cache-heavy agent loop: $0.2 vs $0.0975.
Context tradeoff
GPT-5.6 Terra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence24 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.6 Terra—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Muse Spark 1.259.3%
    Source

    GPT-5.6 Terra leads this result

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Terra64.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.6 Terra—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Muse Spark 1.2—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Muse Spark 1.2—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Muse Spark 1.2?

GPT-5.6 Terra has the higher public score estimate, 72.28 versus 60.25, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.6 Terra or Muse Spark 1.2?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-5.6 Terra or Muse Spark 1.2?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-5.6 Terra or Muse Spark 1.2?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.00337 on Muse Spark 1.2; repository review costs $0.136 and $0.07525; the cache-heavy agent loop costs $0.2 and $0.0975. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Terra or Muse Spark 1.2?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

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Last updated August 5, 2026

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