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BenchLM

Gemini 3.7 Flash vs GPT-5.6 Terra

Updated September 27, 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.

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Decision reading

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 67.66, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini 3.7 Flash

Google

67.66/100

Supported · Public rank #19

90% interval 63.0–72.3

Model B
OpenAI logo
GPT-5.6 Terra

OpenAI

72.58/100

Supported · Public rank #9

90% interval 68.5–76.7

Shared results
14
Gemini 3.7 Flash only
11
GPT-5.6 Terra only
18
Like-for-like categories
3 / 8
Supported: Gemini 3.7 Flash 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 Terra

    GPT-5.6 Terra leads on the public coding lane, 64.7 to 60.3, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Agentic work

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

    GPT-5.6 Terra

    GPT-5.6 Terra leads on the public agentic lane, 59.5 to 58.6, with Supported evidence for both models, although the 90% intervals overlap.

    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

    Gemini 3.7 Flash

    Gemini 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

    Gemini 3.7 Flash

    Gemini 3.7 Flash 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

    Gemini 3.7 Flash

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

    Confidence: listed-rates

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.

60.3Gemini 3.7 Flash64.7GPT-5.6 Terra

Like-for-like · BenchAlign v5.7

GPT-5.6 Terra 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 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 v5.7 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.

Agentic

Like-for-like
Gemini 3.7 Flash
58.6
Supported · #20/105
GPT-5.6 Terra
59.5
Supported · #18/105
Basis
BenchAlign v5.7 lane · 7 vs 9 public rows
Reading
GPT-5.6 Terra leads · intervals overlap

Coding

Like-for-like
Gemini 3.7 Flash
60.3
Supported · #17/135
GPT-5.6 Terra
64.7
Supported · #8/135
Basis
BenchAlign v5.7 lane · 6 vs 8 public rows
Reading
GPT-5.6 Terra leads · intervals overlap

Knowledge

Like-for-like
Gemini 3.7 Flash
71.1
Supported · #10/158
GPT-5.6 Terra
71.1
Supported · #9/158
Basis
BenchAlign v5.7 lane · 6 vs 8 public rows
Reading
Tie

Multimodal

Directional only
Gemini 3.7 Flash
82.6
#10/50
GPT-5.6 Terra
77.2
#16/50
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.7 Flash
77.8
Unranked · 5 rankable rows
GPT-5.6 Terra
65.4
#12/19
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.7 Flash
Not ranked
GPT-5.6 Terra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.7 Flash
Not ranked
GPT-5.6 Terra
85.7
#35/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.7 Flash
Not ranked
GPT-5.6 Terra
96.8
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
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

Gemini 3.7 Flash
$0.00262
Fits in one request
GPT-5.6 Terra
$0.008
Fits in one request

Gemini 3.7 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.7 Flash
$0.04875
Fits in one request
GPT-5.6 Terra
$0.136
Fits in one request

Gemini 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

Gemini 3.7 Flash
$0.0675
Fits in one request
GPT-5.6 Terra
$0.2
Fits in one request

Gemini 3.7 Flash 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.

Gemini 3.7 Flash

$0.075 per 1M cached input tokens

Google Gemini API pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Reasoning profile

Gemini 3.7 Flash

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

Gemini 3.7 Flash

Proprietary

GPT-5.6 Terra

Proprietary

License

Gemini 3.7 Flash

Proprietary

GPT-5.6 Terra

Proprietary

Release date

Gemini 3.7 Flash

2026-08-13

GPT-5.6 Terra

2026-07-09

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, 72.58 versus 67.66, but the 90% score intervals overlap.
Workload cost
Repository review: $0.04875 vs $0.136. Cache-heavy agent loop: $0.0675 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.

Questions

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

GPT-5.6 Terra has the higher public score estimate, 72.58 versus 67.66, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

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

GPT-5.6 Terra leads the public coding lane, 64.7 to 60.3, with Supported evidence for both models, although the 90% intervals overlap.

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

GPT-5.6 Terra leads the public agentic tasks lane, 59.5 to 58.6, with Supported evidence for both models, although the 90% intervals overlap.

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

For the stated presets, chat costs $0.00263 on Gemini 3.7 Flash and $0.008 on GPT-5.6 Terra; repository review costs $0.04875 and $0.136; the cache-heavy agent loop costs $0.0675 and $0.2. Costs use the listed standard API rates.

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

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

Benchmark evidence

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

Browse raw public benchmark evidence43 rows

Agentic

  • Terminal-Bench 2.1

    Gemini 3.7 Flash85.8%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • Terminal-Bench 3.0

    Gemini 3.7 Flash14.9%
    Source
    GPT-5.6 Terra20.8%
    Source

    GPT-5.6 Terra leads this result

  • AutomationBench

    Gemini 3.7 Flash30.4%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • OSWorld 2.0

    Gemini 3.7 Flash47.9%
    Source
    GPT-5.6 Terra50.2%
    Source

    GPT-5.6 Terra leads this result

  • Agents' Last Exam

    Gemini 3.7 Flash26.3%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.7 Flash77.5%
    Source
    GPT-5.6 Terra77.5%
    Source

    Tie

  • ApprenticeBench

    Shared source
    Gemini 3.7 Flash16%
    GPT-5.6 Terra16%

    Tie

  • BrowseComp

    Gemini 3.7 Flash—
    GPT-5.6 Terra87.5%
    Source

    Not directly comparable

  • CyberGym

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

    Not directly comparable

  • ExploitGym

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

    Not directly comparable

  • Toolathlon

    Gemini 3.7 Flash—
    GPT-5.6 Terra53.1%
    Source

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Gemini 3.7 Flash43.6%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • DeepSWE

    Gemini 3.7 Flash65.3%
    Source
    GPT-5.6 Terra69.6%
    Source

    GPT-5.6 Terra leads this result

  • Terminal-Bench 2.1

    Gemini 3.7 Flash85.8%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • FrontierSWE v2

    Gemini 3.7 Flash20.3%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.7 Flash88.7%
    Source
    GPT-5.6 Terra85.9%
    Source

    Gemini 3.7 Flash leads this result

  • SWE-bench (Vals)

    Gemini 3.7 Flash80.8%
    Source
    GPT-5.6 Terra95.4%
    Source

    GPT-5.6 Terra leads this result

  • SWE-bench Pro

    Gemini 3.7 Flash—
    GPT-5.6 Terra63.4%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

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

    Not directly comparable

  • cursorBench32

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

    Not directly comparable

  • VulcanBench v3

    Gemini 3.7 Flash—
    GPT-5.6 Terra87.0%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 64K-128K

    Gemini 3.7 Flash97%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ARC-AGI-1

    Gemini 3.7 Flash95.50%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.7 Flash84.6%
    Source
    GPT-5.6 Terra83.9%
    Source

    Gemini 3.7 Flash leads this result

  • ARC-AGI-3

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

    Not directly comparable

Multimodal

  • CharXiv w/o tools

    Gemini 3.7 Flash84.5%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • CharXiv

    Gemini 3.7 Flash88.7%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • LVBench

    Gemini 3.7 Flash85.4%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • MMMU-Pro

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

    Not directly comparable

  • MMMU-Pro w/ Python

    Gemini 3.7 Flash—
    GPT-5.6 Terra82%
    Source

    Not directly comparable

Knowledge

  • HLE-Verified

    Shared source
    Gemini 3.7 Flash53.6%
    GPT-5.6 Terra51.1%

    Gemini 3.7 Flash leads this result

  • LABBench2

    Shared source
    Gemini 3.7 Flash82.1%
    GPT-5.6 Terra81.2%

    Gemini 3.7 Flash leads this result

  • BioMysteryBench (human-solvable)

    Gemini 3.7 Flash87.1%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Gemini 3.7 Flash43.5%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.7 Flash93.9%
    Source
    GPT-5.6 Terra90.9%
    Source

    Gemini 3.7 Flash leads this result

  • MMLU-Pro (Vals)

    Gemini 3.7 Flash90.1%
    Source
    GPT-5.6 Terra86.7%
    Source

    Gemini 3.7 Flash leads this result

  • GPQA

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

    Not directly comparable

  • GPQA-D

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

    Not directly comparable

  • HealthBench Professional

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

    Not directly comparable

  • HealthBench Hard

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

    Not directly comparable

Math

  • FrontierMath (legacy)

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

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

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

    Not directly comparable

  • FrontierMath v2 (Tier 4)

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

    Not directly comparable

43 public results · 14 shared

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Last updated September 27, 2026