Skip to main content
BenchLM
Data

Grok 4.7 vs o1

Updated November 5, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInAPI/MCP

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
xAI logo
Grok 4.7

xAI

—

Evidence status unavailable

90% interval unavailable

Model B
OpenAI logo
o1

OpenAI

38.54/100

Estimated · Public rank #146

Conditional range 24.2–52.9

Shared results
1
Grok 4.7 only
18
o1 only
3
Like-for-like categories
0 / 8
Estimated: o1. Conditional ranges do not establish rank confidence.How 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.

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.7

    Grok 4.7 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Grok 4.7

    Grok 4.7 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

    Grok 4.7

    Grok 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Grok 4.7 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Grok 4.7 and o1 are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

—Grok 4.730.2o1

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

  • FrontierMath v2 (Tiers 1-3)Math

    Normalized gap 38.2
    Grok 4.7:52.982%
    o1:14.737%
    Shared source
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, 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

Not comparable
Grok 4.7
Not ranked
o1
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Grok 4.7
Not ranked
o1
30.2
Estimated · #103/156
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Grok 4.7
58.8
#24/26
o1
69.7
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Grok 4.7
Not ranked
o1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Grok 4.7
Not ranked
o1
41.2
Supported · #111/177
Basis
BenchAlign v5.8 lane · 1 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.7
Not ranked
o1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.7
Not ranked
o1
86.0
#49/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Grok 4.7
58.6
Unranked · 2 rankable rows
o1
33.5
Unranked · 1 rankable row
Basis
Provisional lane · 2 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, 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

Grok 4.7
$0.005
Fits in one request
o1
$0.045
Fits in one request

Grok 4.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Grok 4.7
$0.118
Fits in one request
o1
$0.93
Fits in one request

Grok 4.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Grok 4.7
$0.2
Fits in one request
o1
$3.90
Does not fit in one request
Cached input priced at the published list-input rate

o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.

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.

Grok 4.7

$0.5 per 1M cached input tokens

xAI Grok 4.7 model documentation

o1

Not published

Provider availability

Grok 4.7

Generally Available · xAI API, xAI US regional endpoint, Grok Build, Cursor, model gateways

xAI Grok 4.7 model documentation

o1

Not sourced

Reasoning profile

Grok 4.7

Reasoning

o1

Reasoning

Weight access

Grok 4.7

Proprietary

o1

Proprietary

License

Grok 4.7

Proprietary

o1

Proprietary

Release date

Grok 4.7

2026-09-21

o1

2024-12-01

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.118 vs $0.93. Cache-heavy agent loop: $0.2 vs $3.90.
Context tradeoff
Grok 4.7 has the larger documented window (500K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Grok 4.7 or o1?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok 4.7 or o1?

Grok 4.7 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Grok 4.7 or o1?

Grok 4.7 and o1 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok 4.7 or o1?

For the stated presets, chat costs $0.005 on Grok 4.7 and $0.045 on o1; repository review costs $0.118 and $0.93; the cache-heavy agent loop costs $0.2 and $3.90. o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Grok 4.7 or o1?

Grok 4.7 has the larger documented context window: 500K, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence22 rows

Agentic

  • Terminal-Bench 4.0

    Grok 4.738.00%
    Source
    o1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Grok 4.773.4%
    Source
    o1—

    Not directly comparable

  • CWE-bench v1

    Grok 4.768.0%
    Source
    o1—

    Not directly comparable

  • Hermes Index

    Grok 4.739.3%
    Source
    o1—

    Not directly comparable

  • Hermes Bench

    Grok 4.774.4%
    Source
    o1—

    Not directly comparable

  • MazeBench

    Grok 4.71%
    Source
    o1—

    Not directly comparable

Coding

  • CursorBench 4.0

    Grok 4.746.3%
    Source
    o1—

    Not directly comparable

  • DeepSWE

    Grok 4.771.0%
    Source
    o1—

    Not directly comparable

  • EEBench

    Grok 4.764.0%
    Source
    o1—

    Not directly comparable

  • FrontierSWE v2

    Grok 4.729.5%
    Source
    o1—

    Not directly comparable

  • Bug Hunt Bench

    Grok 4.729.0 fixes
    Source
    o1—

    Not directly comparable

  • FrontierCode 2.0

    Grok 4.728.6%
    Source
    o1—

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Grok 4.789.50%
    Source
    o1—

    Not directly comparable

  • ARC-AGI-2

    Grok 4.761.4%
    Source
    o1—

    Not directly comparable

  • ARC-AGI-3

    Grok 4.71.8%
    Source
    o1—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    Grok 4.77.02%
    Source
    o1—

    Not directly comparable

Knowledge

  • HealthBench Professional

    Grok 4.756.7%
    Source
    o1—

    Not directly comparable

  • MMLU

    Grok 4.7—
    o191.8%
    Source

    Not directly comparable

  • GPQA

    Grok 4.7—
    o175.7%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Grok 4.7—
    o192.2%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Grok 4.752.982%
    o114.737%

    Grok 4.7 leads this result

  • FrontierMath v2 (Tier 4)

    Grok 4.717.073%
    Source
    o1—

    Not directly comparable

22 public results · 1 shared

Related comparisons

Compare API pricingRead the methodologyOpen the model selector

Watch Grok 4.7 vs o1

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 5, 2026