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o4 Mini High vs Relace Search

Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

o4 Mini HighOpenAI
Coding
No data
Overall
No data
Output $/M
$4.40
Relace SearchRelace
Coding
No data
Overall
No data
Output $/M
$3.00
vs $4.40 vs $3.00 per 1M output tokens. Relace Search is 1.5x cheaper.

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. o4 Mini High has results on ALE-Bench and GDPval; Relace Search has none yet. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
o4 Mini High not benchmarked, Relace Search not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
o4 Mini High $4.40 against Relace Search $3.00 per million output tokens, 1.5x apart.
Context window
o4 Mini High takes 200K tokens, Relace Search 256K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board measured
Coding benchmarks for o4 Mini High and Relace Search
Benchmarko4 Mini HighRelace SearchLeader
ALE-Bench826.2Not measuredOnly o4 Mini High measured
Neither model has a result on the other 37 boards here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Agentic work benchmarks for o4 Mini High and Relace Search
Benchmarko4 Mini HighRelace SearchLeader
GDPval25.3%Not measuredOnly o4 Mini High measured
Neither model has a result on the other 38 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

o4 Mini High$0.0033Fits in one request
Relace Search$0.0025Fits in one request

Relace Search costs less

Repository review

50K input + 3K output tokens

o4 Mini High$0.068Fits in one request
Relace Search$0.059Fits in one request

Relace Search costs less

Agent loop

200K input + 10K output tokens

o4 Mini High$0.264Exceeds the context window
Relace Search$0.230Fits in one request

Relace Search costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of o4 Mini High and Relace Search
SpecificationOpenAI o4 Mini HighRelace Search
MakerOpenAIRelace
WeightsNo public weights listedNo public weights listed
ReleasedApril 16, 2025December 8, 2025
Context window200K256K
Input $/Mtok$1.10$1.00
Output $/Mtok$4.40$3.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o4 Mini High better than Relace Search for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, o4 Mini High or Relace Search?

o4 Mini High costs $1.10 per million input tokens and $4.40 per million output tokens; Relace Search costs $1.00 and $3.00. Cost per solved task is known for neither of them.

Which has the bigger context window, o4 Mini High or Relace Search?

Relace Search, with 256K tokens against 200K.

Has any benchmark tested both o4 Mini High and Relace Search?

No benchmark has tested both yet. o4 Mini High has results on ALE-Bench and GDPval; Relace Search has none yet. Compare them on price and context instead. Data as of October 21, 2026.

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