Skip to main content
Radar

Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.

See Radar
BenchLM
Advertise

Model comparison

GPT-5.5 vs GPT-5.6 Terra

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.5

OpenAI

72.3/100

Estimated · Public rank #11

90% interval 63.3–81.4

GPT-5.6 Terra

OpenAI

72.3/100

Estimated · Public rank #12

90% interval 62.6–82.0

GPT-5.5 has the higher public score estimate, 72.31 versus 72.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

18 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-5.6 Terra

    GPT-5.6 Terra leads on the same 1 weighted benchmark row.

    Confidence: limited

  • 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

    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

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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
18
GPT-5.5 only
15
GPT-5.6 Terra only
4
Like-for-like categories
3 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Like-for-like
GPT-5.5
58.6
GPT-5.6 Terra
63.4
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Reasoning

Like-for-like
GPT-5.5
85.0
GPT-5.6 Terra
83.9
Weighted basis
1 vs 1 rows
Reading
GPT-5.5 leads

Math

Like-for-like
GPT-5.5
47.6
GPT-5.6 Terra
80.8
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Terra leads

Agentic

Directional only
GPT-5.5
81.6
GPT-5.6 Terra
87.4
Weighted basis
3 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.5
57.8
GPT-5.6 Terra
92.9
Weighted basis
2 vs 1 rows
Reading
Directional only

Multimodal

Directional only
GPT-5.5
70.4
GPT-5.6 Terra
80.7
Weighted basis
2 vs 1 rows
Reading
Directional only

Multilingual

Not comparable
GPT-5.5
Not measured
GPT-5.6 Terra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.5
Not measured
GPT-5.6 Terra
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.

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.

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-5.5
$0.02
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-5.5
$0.34
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-5.5
$0.5
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.

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-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Provider availability

GPT-5.5

Not sourced

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GPT-5.5

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

GPT-5.5

Proprietary

GPT-5.6 Terra

Proprietary

License

GPT-5.5

Proprietary

GPT-5.6 Terra

Proprietary

Release date

GPT-5.5

2026-04-23

GPT-5.6 Terra

2026-07-09

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-5.5 has the higher public score estimate, 72.31 versus 72.29, but the 90% score intervals overlap.
Workload cost
Repository review: $0.34 vs $0.136. Cache-heavy agent loop: $0.5 vs $0.2.
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 evidence37 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.582%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • CyberGym

    GPT-5.581.8%
    Source
    GPT-5.6 Terra81.8%
    Source

    Tie

  • BrowseComp

    GPT-5.584.4%
    Source
    GPT-5.6 Terra87.5%
    Source

    GPT-5.6 Terra leads this result

  • OSWorld-Verified

    GPT-5.578.7%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • MCP Atlas

    GPT-5.575.3%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • Toolathlon

    GPT-5.555.6%
    Source
    GPT-5.6 Terra53.1%
    Source

    GPT-5.5 leads this result

  • τ²-bench results

    GPT-5.598%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • Gert Labs

    GPT-5.572.93%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ResearchClawBench

    GPT-5.517.0%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.513.0%
    Source
    GPT-5.6 Terra50.2%
    Source

    GPT-5.6 Terra leads this result

  • JobBench

    GPT-5.542.7%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ExploitGym

    GPT-5.513.4%
    Source
    GPT-5.6 Terra23.2%
    Source

    GPT-5.6 Terra leads this result

Coding

  • SWE-bench Pro

    GPT-5.558.6%
    Source
    GPT-5.6 Terra63.4%
    Source

    GPT-5.6 Terra leads this result

  • Terminal-Bench 2.0

    GPT-5.582.0%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • Vibe Code Bench

    GPT-5.569.85%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • React Native Evals

    GPT-5.584.7%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • cursorBench31

    GPT-5.559.2%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • cursorBench32

    Shared source
    GPT-5.558.4%
    GPT-5.6 Terra64.9%

    GPT-5.6 Terra leads this result

  • FrontierCode 1.1 Main

    GPT-5.543.0%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • deepSwe

    GPT-5.5—
    GPT-5.6 Terra69.6%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.5—
    GPT-5.6 Terra55.8%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 64K-128K

    GPT-5.583.1%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • MRCR v2 128K-256K

    GPT-5.587.5%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ARC-AGI-2

    GPT-5.585%
    Source
    GPT-5.6 Terra83.9%
    Source

    GPT-5.5 leads this result

  • ARC-AGI-3

    GPT-5.50.4%
    Source
    GPT-5.6 Terra0.8%
    Source

    GPT-5.6 Terra leads this result

Knowledge

  • GPQA

    GPT-5.593.6%
    Source
    GPT-5.6 Terra92.9%
    Source

    GPT-5.5 leads this result

  • GPQA-D

    GPT-5.593.6%
    Source
    GPT-5.6 Terra92.9%
    Source

    GPT-5.5 leads this result

  • HLE

    GPT-5.552.2%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • HLE w/o tools

    GPT-5.541.4%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • HealthBench Professional

    GPT-5.5—
    GPT-5.6 Terra57.7%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-5.5—
    GPT-5.6 Terra32.7%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.551.7%
    Source
    GPT-5.6 Terra84.9%
    Source

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.551.700%
    Source
    GPT-5.6 Terra84.900%
    Source

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tier 4)

    GPT-5.535.400%
    Source
    GPT-5.6 Terra68.300%
    Source

    GPT-5.6 Terra leads this result

Multimodal

  • MMMU-Pro

    GPT-5.581.2%
    Source
    GPT-5.6 Terra80.7%
    Source

    GPT-5.5 leads this result

  • MMMU-Pro w/ Python

    GPT-5.583.2%
    Source
    GPT-5.6 Terra82%
    Source

    GPT-5.5 leads this result

  • OfficeQA Pro

    GPT-5.554.1%
    Source
    GPT-5.6 Terra—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.5 or GPT-5.6 Terra?

GPT-5.5 has the higher public score estimate, 72.31 versus 72.29, 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-5.5 or GPT-5.6 Terra?

GPT-5.6 Terra leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, GPT-5.5 or GPT-5.6 Terra?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.5 or GPT-5.6 Terra?

For the stated presets, chat costs $0.02 on GPT-5.5 and $0.008 on GPT-5.6 Terra; repository review costs $0.34 and $0.136; the cache-heavy agent loop costs $0.5 and $0.2. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.5 or GPT-5.6 Terra?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

Watch GPT-5.5 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 2,000+ readers.