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GPT-5 Image Mini vs Saba

Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GPT-5 Image MiniOpenAI
Coding
No data
Overall
No data
Output $/M
$2.00
SabaMistral AI
Coding
No data
Overall
6.5
Output $/M
$0.60
vs $2.00 vs $0.60 per 1M output tokens. Saba is 3.3x cheaper.

Decision reading

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

GPT-5 Image Mini has the edge where the context window is larger, 400K against 33K tokens.

Shared results
0
GPT-5 Image Mini only
3
Saba only
4
Like-for-like categories
0 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
GPT-5 Image Mini not benchmarked, Saba not benchmarked.
Widest category
No category has a result for both models.
Widest single gap
No benchmark has a result for both models.
Output price
GPT-5 Image Mini $2.00 against Saba $0.60 per million output tokens, 3.3x apart.
Context window
GPT-5 Image Mini takes 400K tokens, Saba 33K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

CodingNot comparableSix of these boards feed the Vibe Coding Index0 boards, none shared
Coding benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Neither model has a result on 38 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Intelligence benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
AA Intelligence IndexNot measured6.5Only Saba measured
Neither model has a result on the other 1 board here
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Agentic work benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Neither model has a result on 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Reasoning benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
GPQA Diamond (Artificial Analysis run)Not measured42.4%Only Saba measured
Humanity's Last Exam (Artificial Analysis run)Not measured4.3%Only Saba measured
Neither model has a result on the other 19 boards here
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Math benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Neither model has a result on 13 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Knowledge benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
MMLU Pro (Artificial Analysis run)Not measured61.1%Only Saba measured
Neither model has a result on the other 16 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Neither model has a result on 9 boards here
GeneralNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
General benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Neither model has a result on 46 boards here
Image generationDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Image generation benchmarks for GPT-5 Image Mini and Saba
BenchmarkGPT-5 Image MiniSabaLeader
Design Arena: images1184Not measuredOnly GPT-5 Image Mini measured
Design Arena: graphic design1165Not measuredOnly GPT-5 Image Mini measured
Design Arena: logos1209Not measuredOnly GPT-5 Image Mini measured
Neither model has a result on the other 2 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

GPT-5 Image Mini$0.0035Fits in one request
Saba$0.0005Fits in one request

Saba costs less

Repository review

50K input + 3K output tokens

GPT-5 Image Mini$0.131Fits in one request
Saba$0.012Exceeds the context window

Saba costs less

Agent loop

200K input + 10K output tokens

GPT-5 Image Mini$0.520Fits in one request
Saba$0.046Exceeds the context window

Saba 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 GPT-5 Image Mini and Saba
SpecificationOpenAI GPT-5 Image MiniMistral Saba
MakerOpenAIMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedOctober 16, 2025February 17, 2025
Context window400K33K
Input $/Mtok$2.50$0.20
Output $/Mtok$2.00$0.60
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 Image Mini better than Saba for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5 Image Mini has the edge where the context window is larger, 400K against 33K tokens.

Which is cheaper, GPT-5 Image Mini or Saba?

GPT-5 Image Mini costs $2.50 per million input tokens and $2.00 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-5 Image Mini or Saba?

GPT-5 Image Mini, with 400K tokens against 33K.

How many benchmarks is the GPT-5 Image Mini vs Saba comparison based on?

0 benchmarks have a result for both models as of October 21, 2026. GPT-5 Image Mini's coding index rests on 0 independent benchmarks and Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.

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