Model comparison

GPT-5.3 Codex vs Inkling-Small

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.3 Codex

OpenAI

65.8/100

Supported · Public rank #30

90% interval 62.3–69.2

Inkling-Small

Thinking Machines Lab

—

Evidence status unavailable

90% interval unavailable

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

3 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.

Long documents
Prompts that approach the documented context limit
Inkling-Small
Inkling-Small has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Inkling-Small
Inkling-Small 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
Inkling-Small
Inkling-Small has the lower estimated token cost for this stated workload. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Inkling-Small
Inkling-Small 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
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.

5 GPT-5.3 Codex only

3 Shared

17 Inkling-Small only

Shared results: 3
GPT-5.3 Codex only: 5
Inkling-Small only: 17
Like-for-like categories: 0 / 8

2 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.

Agentic

Directional only

GPT-5.3 Codex: 71.4
Inkling-Small: 70.1
Weighted basis: 2 vs 2 rows
Reading: Directional only

Coding

Directional only

GPT-5.3 Codex: 67.2
Inkling-Small: 62.4
Weighted basis: 3 vs 3 rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: 40.1
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Knowledge

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: 53.4
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: 92.9
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: 76.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.3 Codex: Not measured
Inkling-Small: 82.2
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.3 Codex and Inkling-Small
Category	GPT-5.3 Codex	Inkling-Small	Weighted basis	Reading
Agentic	71.4	70.1	Directional only2 vs 2 rows	Directional only
Coding	67.2	62.4	Directional only3 vs 3 rows	Directional only
Reasoning	Not measured	40.1	Not comparable0 vs 1 rows	Not comparable
Knowledge	Not measured	53.4	Not comparable0 vs 2 rows	Not comparable
Math	Not measured	92.9	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	76.6	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	82.2	Not comparable0 vs 1 rows	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.

Terminal-Bench 2.0
Agentic
GPT-5.3 Codex: 77.3%Inkling-Small: 64.7%Normalized gap 12.6GPT-5.3 Codex source Inkling-Small source
SWE-bench Verified
Coding
GPT-5.3 Codex: 85%Inkling-Small: 80.2%Normalized gap 4.8GPT-5.3 Codex source Inkling-Small source
SWE-bench Pro
Coding
GPT-5.3 Codex: 56.8%Inkling-Small: 55.9%Normalized gap 0.9GPT-5.3 Codex source Inkling-Small source

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-5.3 Codex: $0.00875; Fits in one request
Inkling-Small: $0.0013; Fits in one request

Inkling-Small has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.3 Codex: $0.1295; Fits in one request
Inkling-Small: $0.03332; Fits in one request

Inkling-Small 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-5.3 Codex: $0.525; Fits in one request; Cached input priced at the published list-input rate
Inkling-Small: $0.0492; Fits in one request

GPT-5.3 Codex

Reasoning

Inkling-Small

Hybrid

Weight access

GPT-5.3 Codex

Proprietary

Inkling-Small

Open Weight

License

GPT-5.3 Codex

Proprietary

Inkling-Small

Open Weight

Release date

GPT-5.3 Codex

2026-02-05

Inkling-Small

2026-07-30

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: $0.1295 vs $0.03332. Cache-heavy agent loop: $0.525 vs $0.0492.
Context tradeoff: Inkling-Small has the larger documented window (1M).

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 evidence25 rows

Agentic

Terminal-Bench 2.0
GPT-5.3 Codex77.3%
Source
Inkling-Small64.7%
Source
GPT-5.3 Codex leads this result
OSWorld-Verified
GPT-5.3 Codex64.7%
Source
Inkling-Small—
Not directly comparable
Gert Labs
GPT-5.3 Codex57.47%
Source
Inkling-Small—
Not directly comparable
JobBench
GPT-5.3 Codex33.7%
Source
Inkling-Small—
Not directly comparable
BrowseComp
GPT-5.3 Codex—
Inkling-Small77.4%
Source
Not directly comparable
MCP Atlas
GPT-5.3 Codex—
Inkling-Small79.6%
Source
Not directly comparable
Toolathlon-Verified
GPT-5.3 Codex—
Inkling-Small54.4%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-5.3 Codex85%
Source
Inkling-Small80.2%
Source
GPT-5.3 Codex leads this result
SWE-bench Pro
GPT-5.3 Codex56.8%
Source
Inkling-Small55.9%
Source
GPT-5.3 Codex leads this result
SWE-Rebench
GPT-5.3 Codex58.2%
Source
Inkling-Small—
Not directly comparable
Vibe Code Bench
GPT-5.3 Codex61.77%
Source
Inkling-Small—
Not directly comparable
Terminal-Bench 2.0
GPT-5.3 Codex—
Inkling-Small64.7%
Source
Not directly comparable
SciCode
GPT-5.3 Codex—
Inkling-Small48.7%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.3 Codex—
Inkling-Small40.1%
Source
Not directly comparable
CritPt
GPT-5.3 Codex—
Inkling-Small8.3%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.3 Codex—
Inkling-Small89.5%
Source
Not directly comparable
GPQA-D
GPT-5.3 Codex—
Inkling-Small89.5%
Source
Not directly comparable
HLE
GPT-5.3 Codex—
Inkling-Small47.8%
Source
Not directly comparable
HLE w/o tools
GPT-5.3 Codex—
Inkling-Small31.6%
Source
Not directly comparable

Math

AIME26
GPT-5.3 Codex—
Inkling-Small95.5%
Source
Not directly comparable
HMMT Feb 2026
GPT-5.3 Codex—
Inkling-Small90.2%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.3 Codex—
Inkling-Small74%
Source
Not directly comparable
CharXiv
GPT-5.3 Codex—
Inkling-Small81.3%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.3 Codex—
Inkling-Small77.4%
Source
Not directly comparable

Instruction following

IFBench
GPT-5.3 Codex—
Inkling-Small82.2%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.3 Codex or Inkling-Small?

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, GPT-5.3 Codex or Inkling-Small?

The current coding 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 is better for agentic tasks, GPT-5.3 Codex or Inkling-Small?

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-5.3 Codex or Inkling-Small?

For the stated presets, chat costs $0.00875 on GPT-5.3 Codex and $0.0013 on Inkling-Small; repository review costs $0.1295 and $0.03332; the cache-heavy agent loop costs $0.525 and $0.0492. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.3 Codex or Inkling-Small?

Inkling-Small has the larger documented context window: 1M, compared with 400K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 30, 2026

Watch GPT-5.3 Codex vs Inkling-Small

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

Read a sample issue

Join 2,000+ readers.

GPT-5.3 Codex vs Inkling-Small

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Frequently asked questions

Which is better, GPT-5.3 Codex or Inkling-Small?

Which is better for coding, GPT-5.3 Codex or Inkling-Small?

Which is better for agentic tasks, GPT-5.3 Codex or Inkling-Small?

Which costs less, GPT-5.3 Codex or Inkling-Small?

Which has the larger context window, GPT-5.3 Codex or Inkling-Small?

Related comparisons

Watch GPT-5.3 Codex vs Inkling-Small