GPT-5.1-Codex-Mini vs Reka Flash 3
GPT-5.1-Codex-Mini leads Reka Flash 3 on the Artificial Analysis Intelligence Index, 20.4 to 6.4, and Reka Flash 3 costs 10.0x less per output token ($0.20 vs $2.00 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Reka Flash 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for a larger context window (400K against 66K tokens).
- Shared results
- 11
- GPT-5.1-Codex-Mini only
- 11
- Reka Flash 3 only
- 1
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 6.4.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Reka Flash 3
GPT-5.1-Codex-Mini $0.0013, Reka Flash 3 $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Reka Flash 3 not benchmarked.
- Widest category
- Agentic work, 62.9 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.1-Codex-Mini 66.7% against Reka Flash 3 0.0%, 66.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Reka Flash 3 $0.20 per million output tokens, 10.0x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Reka Flash 3 66K.
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.
| Category | GPT-5.1-Codex-Mini | Bars | Reka Flash 3 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 11 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | 43.5% | GPT-5.1-Codex-Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 0.0% | GPT-5.1-Codex-Mini |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 6.4 | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 0.0% | GPT-5.1-Codex-Mini |
| BALROG | Not measured | 29.2% | Only Reka Flash 3 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 52.9% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 4.4% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 20 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | 33.7 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 91.7% | 33.7% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | 66.9% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | 1078 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | 1092 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | 1097 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1093 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| IFBench | 67.9% | 30.4% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 0.0% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Reka Flash 3 | Leader |
|---|---|---|---|
| Neither model has a result on 5 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
Reka Flash 3 costs less
Repository review
50K input + 3K output tokens
Reka Flash 3 costs less
Agent loop
200K input + 10K output tokens
Reka Flash 3 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.
| Specification | OpenAI GPT-5.1-Codex-Mini | Reka Flash 3 |
|---|---|---|
| Maker | OpenAI | Reka |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | March 12, 2025 |
| Context window | 400K | 66K |
| Input $/Mtok | $0.25 | $0.10 |
| Output $/Mtok | $2.00 | $0.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than Reka Flash 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for a larger context window (400K against 66K tokens).
Which is cheaper, GPT-5.1-Codex-Mini or Reka Flash 3?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Reka Flash 3 costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Reka Flash 3?
GPT-5.1-Codex-Mini, with 400K tokens against 66K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Reka Flash 3 comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Reka Flash 3's on 0. Where only one model has a result, the page says so instead of naming a winner.