Skip to main content
BenchLM
Data

GPT-5.6 Sol vs GPT-5.6 Terra

Updated November 9, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.

Share or export
Share on XLinkedInAPI/MCP

Decision reading

GPT-5.6 Sol has the higher public score estimate, 74.36 versus 69.42, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 38 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-5.6 Sol

OpenAI

74.36/100

Supported · Public rank #14

90% interval 71.3–77.5

Model B
OpenAI logo
GPT-5.6 Terra

OpenAI

69.42/100

Supported · Public rank #16

90% interval 65.1–73.8

Shared results
38
GPT-5.6 Sol only
9
GPT-5.6 Terra only
0
Like-for-like categories
5 / 8
Supported: GPT-5.6 Sol and GPT-5.6 TerraHow the comparison works

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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-5.6 Sol

    GPT-5.6 Sol has the higher public coding point estimate, 67.2 to 61.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Agentic work

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

    GPT-5.6 Sol

    GPT-5.6 Sol has the higher public agentic point estimate, 67.9 to 60.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.6 Terra

    GPT-5.6 Terra 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

  • Cache-heavy agent loop cost

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

    GPT-5.6 Terra

    GPT-5.6 Terra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.6 Terra

    GPT-5.6 Terra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

67.2GPT-5.6 Sol61.8GPT-5.6 Terra

Like-for-like · BenchAlign v5.8

GPT-5.6 Sol has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like
GPT-5.6 Sol
67.9
Supported · #14/132
GPT-5.6 Terra
60.4
Supported · #26/132
Basis
BenchAlign v5.8 lane · 12 vs 11 public rows
Reading
GPT-5.6 Sol leads · intervals overlap

Coding

Like-for-like
GPT-5.6 Sol
67.2
Supported · #12/158
GPT-5.6 Terra
61.8
Supported · #18/158
Basis
BenchAlign v5.8 lane · 14 vs 9 public rows
Reading
GPT-5.6 Sol leads · intervals overlap

Reasoning

Like-for-like
GPT-5.6 Sol
73.7
Supported · #12/157
GPT-5.6 Terra
69.1
Supported · #18/157
Basis
BenchAlign v5.8 lane · 5 vs 3 public rows
Reading
GPT-5.6 Sol leads · intervals overlap

Multimodal

Like-for-like
GPT-5.6 Sol
75.4
Supported · #9/67
GPT-5.6 Terra
68.9
Supported · #15/67
Basis
BenchAlign v5.8 lane · 4 vs 4 public rows
Reading
GPT-5.6 Sol leads · intervals overlap

Knowledge

Like-for-like
GPT-5.6 Sol
75.4
Supported · #12/179
GPT-5.6 Terra
68.2
Supported · #20/179
Basis
BenchAlign v5.8 lane · 8 vs 8 public rows
Reading
GPT-5.6 Sol leads · intervals overlap

Instruction following

Directional only
GPT-5.6 Sol
89.1
#34/157
GPT-5.6 Terra
87.1
#42/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Multilingual

Not comparable
GPT-5.6 Sol
Not ranked
GPT-5.6 Terra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Sol
92.3
Unranked · 3 rankable rows
GPT-5.6 Terra
86.6
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 Sol
$0.014
Fits in one request
GPT-5.6 Terra
$0.008
Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol
$0.26
Fits in one request
GPT-5.6 Terra
$0.136
Fits in one request

GPT-5.6 Terra 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 Sol
$0.36
Fits in one request
GPT-5.6 Terra
$0.2
Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Cached input falls back to the list input rate only where a cached rate is unpublished

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 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Reasoning profile

GPT-5.6 Sol

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

GPT-5.6 Terra

Proprietary

License

GPT-5.6 Sol

Proprietary

GPT-5.6 Terra

Proprietary

Release date

GPT-5.6 Sol

2026-07-09

GPT-5.6 Terra

2026-07-09

If you are choosing between sibling variants

Deployment change
Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
GPT-5.6 Sol has the higher public score estimate, 74.36 versus 69.42, but the 90% score intervals overlap.
Workload cost
Repository review: $0.26 vs $0.136. Cache-heavy agent loop: $0.36 vs $0.2.
Context tradeoff
Both models list 1.05M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.6 Sol or GPT-5.6 Terra?

GPT-5.6 Sol has the higher public score estimate, 74.36 versus 69.42, 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 Sol or GPT-5.6 Terra?

GPT-5.6 Sol has the higher public coding point estimate, 67.2 to 61.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-5.6 Sol or GPT-5.6 Terra?

GPT-5.6 Sol has the higher public agentic tasks point estimate, 67.9 to 60.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GPT-5.6 Sol or GPT-5.6 Terra?

For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.008 on GPT-5.6 Terra; repository review costs $0.26 and $0.136; the cache-heavy agent loop costs $0.36 and $0.2. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Sol or GPT-5.6 Terra?

Both models list the same context window, 1.05M.

Benchmark evidence

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

Browse raw public benchmark evidence47 rows

Agentic

  • Terminal-Bench 4.1

    Shared source
    GPT-5.6 Sol38.50%
    GPT-5.6 Terra21.50%

    GPT-5.6 Sol leads this result

  • LifeVal

    GPT-5.6 Sol80.0%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • Terminal-Bench 3.0

    Shared source
    GPT-5.6 Sol34.6%
    GPT-5.6 Terra20.8%

    GPT-5.6 Sol leads this result

  • Terminal-Bench 2.1

    Shared source
    GPT-5.6 Sol91.9%
    GPT-5.6 Terra87.4%

    GPT-5.6 Sol leads this result

  • BrowseComp

    Shared source
    GPT-5.6 Sol92.2%
    GPT-5.6 Terra87.5%

    GPT-5.6 Sol leads this result

  • OSWorld 2.0

    Shared source
    GPT-5.6 Sol62.6%
    GPT-5.6 Terra50.2%

    GPT-5.6 Sol leads this result

  • CyberGym

    Shared source
    GPT-5.6 Sol84.5%
    GPT-5.6 Terra81.8%

    GPT-5.6 Sol leads this result

  • ExploitGym

    Shared source
    GPT-5.6 Sol33.7%
    GPT-5.6 Terra23.2%

    GPT-5.6 Sol leads this result

  • Toolathlon

    Shared source
    GPT-5.6 Sol58%
    GPT-5.6 Terra53.1%

    GPT-5.6 Sol leads this result

  • Terminal-Bench 2.1 (Vals)

    GPT-5.6 Sol85.8%
    Source
    GPT-5.6 Terra77.5%
    Source

    GPT-5.6 Sol leads this result

  • ApprenticeBench

    Shared source
    GPT-5.6 Sol26%
    GPT-5.6 Terra16%

    GPT-5.6 Sol leads this result

  • MazeBench

    GPT-5.6 Sol13%
    Source
    GPT-5.6 Terra6%
    Source

    GPT-5.6 Sol leads this result

Coding

  • Bug Hunt Bench

    GPT-5.6 Sol42.0 fixes
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • SWE-bench Pro

    Shared source
    GPT-5.6 Sol64.6%
    GPT-5.6 Terra63.4%

    GPT-5.6 Sol leads this result

  • Terminal-Bench 2.1

    Shared source
    GPT-5.6 Sol91.9%
    GPT-5.6 Terra87.4%

    GPT-5.6 Sol leads this result

  • DeepSWE

    Shared source
    GPT-5.6 Sol72.7%
    GPT-5.6 Terra69.6%

    GPT-5.6 Sol leads this result

  • FrontierCode 1.1 Extended

    Shared source
    GPT-5.6 Sol60.6%
    GPT-5.6 Terra55.8%

    GPT-5.6 Sol leads this result

  • FrontierSWE v2

    GPT-5.6 Sol32.2%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • CursorBench 3.2

    Shared source
    GPT-5.6 Sol67.2%
    GPT-5.6 Terra64.9%

    GPT-5.6 Sol leads this result

  • VulcanBench v3

    GPT-5.6 Sol87.0%
    Source
    GPT-5.6 Terra87.0%
    Source

    Tie

  • VulcanBench CII v1

    GPT-5.6 Sol86.5%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.6 Sol82.6%
    Source
    GPT-5.6 Terra85.9%
    Source

    GPT-5.6 Terra leads this result

  • SWE-bench (Vals)

    GPT-5.6 Sol96.2%
    Source
    GPT-5.6 Terra95.4%
    Source

    GPT-5.6 Sol leads this result

  • CursorBench 4.0

    Shared source
    GPT-5.6 Sol41.7%
    GPT-5.6 Terra41.3%

    GPT-5.6 Sol leads this result

  • PostTrainBench v1.1

    GPT-5.6 Sol36.2%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • FrontierCode 2.0

    GPT-5.6 Sol27.6%
    Source
    GPT-5.6 Terra—

    Not directly comparable

Reasoning

  • BioBasicsBench

    GPT-5.6 Sol45.6%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ARC-AGI-2

    GPT-5.6 Sol92.5%
    Source
    GPT-5.6 Terra83.9%
    Source

    GPT-5.6 Sol leads this result

  • ARC-AGI-3

    GPT-5.6 Sol7.8%
    Source
    GPT-5.6 Terra0.8%
    Source

    GPT-5.6 Sol leads this result

  • GeneBench-Pro

    GPT-5.6 Sol28.7%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ARC-AGI-1

    GPT-5.6 Sol96.50%
    Source
    GPT-5.6 Terra96.50%
    Source

    Tie

Multimodal

  • ExtractBench (LlamaIndex)

    Shared source
    GPT-5.6 Sol93.77%
    GPT-5.6 Terra92.26%

    GPT-5.6 Sol leads this result

  • ParseBench

    Shared source
    GPT-5.6 Sol74.40%
    GPT-5.6 Terra67.06%

    GPT-5.6 Sol leads this result

  • MMMU-Pro

    Shared source
    GPT-5.6 Sol83%
    GPT-5.6 Terra80.7%

    GPT-5.6 Sol leads this result

  • MMMU-Pro w/ Python

    Shared source
    GPT-5.6 Sol84.6%
    GPT-5.6 Terra82%

    GPT-5.6 Sol leads this result

Knowledge

  • GPQA

    Shared source
    GPT-5.6 Sol94.6%
    GPT-5.6 Terra92.9%

    GPT-5.6 Sol leads this result

  • GPQA-D

    Shared source
    GPT-5.6 Sol94.6%
    GPT-5.6 Terra92.9%

    GPT-5.6 Sol leads this result

  • HLE-Verified

    Shared source
    GPT-5.6 Sol54.5%
    GPT-5.6 Terra51.1%

    GPT-5.6 Sol leads this result

  • LABBench2

    Shared source
    GPT-5.6 Sol82.1%
    GPT-5.6 Terra81.2%

    GPT-5.6 Sol leads this result

  • HealthBench Professional

    Shared source
    GPT-5.6 Sol60.5%
    GPT-5.6 Terra57.7%

    GPT-5.6 Sol leads this result

  • HealthBench Hard

    Shared source
    GPT-5.6 Sol33.1%
    GPT-5.6 Terra32.7%

    GPT-5.6 Sol leads this result

  • GPQA Diamond (Vals)

    GPT-5.6 Sol95.2%
    Source
    GPT-5.6 Terra90.9%
    Source

    GPT-5.6 Sol leads this result

  • MMLU-Pro (Vals)

    GPT-5.6 Sol89.1%
    Source
    GPT-5.6 Terra86.7%
    Source

    GPT-5.6 Sol leads this result

Instruction following

  • Gray Swan IPI (15 attempts)

    GPT-5.6 Sol27.0%
    Source
    GPT-5.6 Terra—

    Not directly comparable

Math

  • FrontierMath (legacy)

    Shared source
    GPT-5.6 Sol89%
    GPT-5.6 Terra84.9%

    GPT-5.6 Sol leads this result

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-5.6 Sol89.000%
    GPT-5.6 Terra84.900%

    GPT-5.6 Sol leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GPT-5.6 Sol83.000%
    GPT-5.6 Terra68.300%

    GPT-5.6 Sol leads this result

47 public results · 38 shared

Related comparisons

Compare API pricingRead the methodologyOpen the model selector

Watch GPT-5.6 Sol vs GPT-5.6 Terra

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 6,100+ subscribers.

Last updated November 9, 2026