GPT-5.6 Terra vs GPT-6.1 Sol

Updated September 29, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Terra has the higher public score estimate, 72.4 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.6 Terra

OpenAI

72.4/100

Supported · Public rank #11

90% interval 67.9–76.9

Model B

GPT-6.1 Sol

OpenAI

66.86/100

Estimated · Public rank #23

90% interval 55.4–78.4

Shared results: 4
GPT-5.6 Terra only: 29
GPT-6.1 Sol only: 5
Like-for-like categories: 1 / 8

Supported: GPT-5.6 Terra · Estimated: GPT-6.1 SolHow 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-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 64.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
GPT-6.1 Sol
GPT-6.1 Sol 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
GPT-6.1 Sol
GPT-6.1 Sol 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
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.

Pick the job you would use them for

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

64.4GPT-5.6 Terra67.0GPT-6.1 Sol

Like-for-like · BenchAlign v5.7

GPT-6.1 Sol leads the like-for-like coding row, although the 90% intervals overlap.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, 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.

Coding

Like-for-like

GPT-5.6 Terra: 64.4; Supported · #10/143
GPT-6.1 Sol: 67.0; Supported · #8/143
Basis: BenchAlign v5.7 lane · 9 vs 1 public rows
Reading: GPT-6.1 Sol leads · intervals overlap

Knowledge

Directional only

GPT-5.6 Terra: 70.8; Supported · #11/169
GPT-6.1 Sol: 71.3; Estimated · #10/169
Basis: BenchAlign v5.7 lane · 8 vs 5 public rows
Reading: Directional only

Agentic

Not comparable

GPT-5.6 Terra: 59.4; Supported · #20/117
GPT-6.1 Sol: Not ranked
Basis: BenchAlign v5.7 lane · 9 vs 3 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.6 Terra: 65.5; #18/27
GPT-6.1 Sol: 79.4; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.6 Terra: 78.2; #16/50
GPT-6.1 Sol: 83.9; Unranked · 1 rankable row
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Terra: Not ranked
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Terra: 85.7; #35/124
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Terra: 96.8; Unranked · 3 rankable rows
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Terra and GPT-6.1 Sol
Category	GPT-5.6 Terra	GPT-6.1 Sol	Basis	Reading
Coding	64.4Supported · #10/143	67.0Supported · #8/143	Like-for-likeBenchAlign v5.7 lane · 9 vs 1 public rows	GPT-6.1 Sol leads · intervals overlap
Knowledge	70.8Supported · #11/169	71.3Estimated · #10/169	Directional onlyBenchAlign v5.7 lane · 8 vs 5 public rows	Directional only
Agentic	59.4Supported · #20/117	Not ranked	Not comparableBenchAlign v5.7 lane · 9 vs 3 public rows	Not comparable
Reasoning	65.5#18/27	79.4Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	78.2#16/50	83.9Unranked · 1 rankable row	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	85.7#35/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	96.8Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 Terra: $0.008; Fits in one request
GPT-6.1 Sol: $0.007; Fits in one request

GPT-6.1 Sol 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
GPT-6.1 Sol: $0.13; Fits in one request

GPT-6.1 Sol 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
GPT-6.1 Sol: $0.16; Fits in one request

GPT-6.1 Sol 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.

SpecificationGPT-5.6 TerraGPT-6.1 Sol

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.6 Terra

1.05M

OpenAI model catalog

GPT-6.1 Sol

1.05M

OpenAI GPT-6.1 Sol model documentation

API model ID

GPT-5.6 Terra

gpt-5.6-terra

OpenAI model catalog

GPT-6.1 Sol

gpt-6.1-sol

OpenAI GPT-6.1 Sol model documentation

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

GPT-6.1 Sol

$0.1 per 1M cached input tokens

OpenAI GPT-6.1 Sol model documentation

Documented inputs

GPT-5.6 Terra

text, image

OpenAI model catalog

GPT-6.1 Sol

text, image

OpenAI GPT-6.1 Sol model documentation

Documented outputs

GPT-5.6 Terra

text

OpenAI model catalog

GPT-6.1 Sol

text

OpenAI GPT-6.1 Sol model documentation

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

GPT-6.1 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API

OpenAI GPT-6.1 Sol model documentation

Reasoning profile

GPT-5.6 Terra

Reasoning

GPT-6.1 Sol

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

GPT-6.1 Sol

Proprietary

License

GPT-5.6 Terra

Proprietary

GPT-6.1 Sol

Proprietary

Release date

GPT-5.6 Terra

2026-07-09

GPT-6.1 Sol

2026-09-29

If you already use one of these models

Deployment change: Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: GPT-5.6 Terra has the higher public score estimate, 72.4 versus 66.86, but the 90% score intervals overlap.
Workload cost: Repository review: $0.136 vs $0.13. Cache-heavy agent loop: $0.2 vs $0.16.
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 Terra or GPT-6.1 Sol?

GPT-5.6 Terra has the higher public score estimate, 72.4 versus 66.86, 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 GPT-6.1 Sol?

GPT-6.1 Sol leads the public coding lane, 67 to 64.4, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.6 Terra or GPT-6.1 Sol?

GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.6 Terra or GPT-6.1 Sol?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.007 on GPT-6.1 Sol; repository review costs $0.136 and $0.13; the cache-heavy agent loop costs $0.2 and $0.16. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Terra or GPT-6.1 Sol?

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 evidence38 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Terra20.8%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
GPT-6.1 Sol—
Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%
Source
GPT-6.1 Sol—
Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%
Source
GPT-6.1 Sol—
Not directly comparable
CyberGym
GPT-5.6 Terra81.8%
Source
GPT-6.1 Sol—
Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%
Source
GPT-6.1 Sol35.1%
Source
GPT-6.1 Sol leads this result
Toolathlon
GPT-5.6 Terra53.1%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra77.5%
Source
GPT-6.1 Sol—
Not directly comparable
ApprenticeBench
GPT-5.6 Terra16%
Source
GPT-6.1 Sol—
Not directly comparable
AutomationBench
GPT-5.6 Terra—
GPT-6.1 Sol36.1%
Source
Not directly comparable
Terminal-Bench-Science 0.1
GPT-5.6 Terra—
GPT-6.1 Sol57.0%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Terra63.4%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
GPT-6.1 Sol—
Not directly comparable
DeepSWE
GPT-5.6 Terra69.6%
Source
GPT-6.1 Sol71.9%
Source
GPT-6.1 Sol leads this result
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%
Source
GPT-6.1 Sol—
Not directly comparable
cursorBench32
GPT-5.6 Terra64.9%
Source
GPT-6.1 Sol—
Not directly comparable
VulcanBench v3
GPT-5.6 Terra87.0%
Source
GPT-6.1 Sol—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Terra85.9%
Source
GPT-6.1 Sol—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Terra95.4%
Source
GPT-6.1 Sol—
Not directly comparable
cursorBench40
GPT-5.6 Terra41.3%
Source
GPT-6.1 Sol—
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Terra83.9%
Source
GPT-6.1 Sol—
Not directly comparable
ARC-AGI-3
GPT-5.6 Terra0.8%
Source
GPT-6.1 Sol—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Terra80.7%
Source
GPT-6.1 Sol—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Terra82%
Source
GPT-6.1 Sol—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Terra92.9%
Source
GPT-6.1 Sol—
Not directly comparable
GPQA-D
GPT-5.6 Terra92.9%
Source
GPT-6.1 Sol—
Not directly comparable
HLE-Verified
GPT-5.6 Terra51.1%
Source
GPT-6.1 Sol—
Not directly comparable
LABBench2
GPT-5.6 Terra81.2%
Source
GPT-6.1 Sol—
Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%
Source
GPT-6.1 Sol64.2%
Source
GPT-6.1 Sol leads this result
HealthBench Hard
GPT-5.6 Terra32.7%
Source
GPT-6.1 Sol36.2%
Source
GPT-6.1 Sol leads this result
GPQA Diamond (Vals)
GPT-5.6 Terra90.9%
Source
GPT-6.1 Sol—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Terra86.7%
Source
GPT-6.1 Sol—
Not directly comparable
HealthBench (raw)
GPT-5.6 Terra—
GPT-6.1 Sol56.7%
Source
Not directly comparable
HealthBench (length-adjusted)
GPT-5.6 Terra—
GPT-6.1 Sol58.5%
Source
Not directly comparable
HealthBench Professional (raw)
GPT-5.6 Terra—
GPT-6.1 Sol67.2%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Terra84.9%
Source
GPT-6.1 Sol—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Terra84.900%
Source
GPT-6.1 Sol—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Terra68.300%
Source
GPT-6.1 Sol—
Not directly comparable

38 public results · 4 shared

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Last updated September 29, 2026

GPT-5.6 Terra vs GPT-6.1 Sol

Decision reading

Which one for your work

Coding work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.6 Terra or GPT-6.1 Sol?

Which is better for coding, GPT-5.6 Terra or GPT-6.1 Sol?

Which is better for agentic tasks, GPT-5.6 Terra or GPT-6.1 Sol?

Which costs less, GPT-5.6 Terra or GPT-6.1 Sol?

Which has the larger context window, GPT-5.6 Terra or GPT-6.1 Sol?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.6 Terra vs GPT-6.1 Sol