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-5.6 Terra vs Step 3.7 Flash

Updated August 10, 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 62.6–82.0

Step 3.7 Flash

StepFun

50.0/100

Estimated · Public rank #119

90% interval 38.4–61.5

GPT-5.6 Terra has the higher public score, 72.29 versus 49.95, and the 90% score intervals do not overlap.

5 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

  • Agentic work

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

    GPT-5.6 Terra

    GPT-5.6 Terra leads on the same 2 weighted benchmark rows.

    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

Show secondary and unsupported calls

  • Chat turn cost

    1K fresh input + 500 output tokens

    Step 3.7 Flash

    Step 3.7 Flash 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

    Step 3.7 Flash

    Step 3.7 Flash has the lower estimated token cost for this stated workload. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Step 3.7 Flash

    Step 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

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
5
GPT-5.6 Terra only
17
Step 3.7 Flash only
6
Like-for-like categories
2 / 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

Like-for-like
GPT-5.6 Terra
87.4
Step 3.7 Flash
66.4
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Terra leads

Coding

Like-for-like
GPT-5.6 Terra
63.4
Step 3.7 Flash
56.3
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Step 3.7 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Terra
92.9
Step 3.7 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Terra
80.8
Step 3.7 Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Step 3.7 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Step 3.7 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Step 3.7 Flash
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-5.6 Terra
$0.008
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

Step 3.7 Flash 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
Step 3.7 Flash
$0.01345
Fits in one request

Step 3.7 Flash 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
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

Step 3.7 Flash has the lower modeled cost

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

Context window

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

GPT-5.6 Terra

1.05M

OpenAI model catalog

Step 3.7 Flash

256K

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

Step 3.7 Flash

Not published

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Step 3.7 Flash

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Step 3.7 Flash

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Step 3.7 Flash

Open Weight

License

GPT-5.6 Terra

Proprietary

Step 3.7 Flash

Open Weight

Release date

GPT-5.6 Terra

2026-07-09

Step 3.7 Flash

2026-05-29

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, 72.29 versus 49.95, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.136 vs $0.01345. Cache-heavy agent loop: $0.2 vs $0.0555.
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 evidence28 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Step 3.7 Flash59.5%
    Source

    GPT-5.6 Terra leads this result

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Step 3.7 Flash75.8%
    Source

    GPT-5.6 Terra leads this result

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Step 3.7 Flash49.5%
    Source

    GPT-5.6 Terra leads this result

  • DeepSearchQA

    GPT-5.6 Terra—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.6 Terra—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • HLE w/ tools

    GPT-5.6 Terra—
    Step 3.7 Flash47.2%
    Source

    Not directly comparable

  • Gert Labs

    GPT-5.6 Terra—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Step 3.7 Flash56.3%
    Source

    GPT-5.6 Terra leads this result

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Step 3.7 Flash59.5%
    Source

    GPT-5.6 Terra leads this result

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Terra64.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Step 3.7 Flash—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SimpleVQA

    GPT-5.6 Terra—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    GPT-5.6 Terra—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Step 3.7 Flash?

GPT-5.6 Terra has the higher public score, 72.29 versus 49.95, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.6 Terra or Step 3.7 Flash?

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

Which is better for agentic tasks, GPT-5.6 Terra or Step 3.7 Flash?

GPT-5.6 Terra leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, GPT-5.6 Terra or Step 3.7 Flash?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.00077 on Step 3.7 Flash; repository review costs $0.136 and $0.01345; the cache-heavy agent loop costs $0.2 and $0.0555. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.6 Terra or Step 3.7 Flash?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

Watch GPT-5.6 Terra vs Step 3.7 Flash

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

Read a sample issue

Join 2,000+ readers.