# GPT-5.3 Codex vs MiMo-V2-Flash

> GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 40.8 on the Noometry Index. MiMo-V2-Flash costs 27× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-3-codex-vs-mimo-v2-flash
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. GPT-5.3 Codex scores higher in 1 category and MiMo-V2-Flash in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.3 Codex leads 48.5 to 35.8.
- MiMo-V2-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 262K.
- MiMo-V2-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 45.5 | 40.8 |
| Rank | 61 | 137 |
| Context | 400K | 262K |
| Input $/M | $1.75 | $0.14 |
| Output $/M | $14 | $0.28 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.3 Codex: 48.5 (#51)
- MiMo-V2-Flash: 35.8 (#205)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena WebDev | 1408 | 1330 |
| ALE-Bench | 1,655 | 737.95 |
| SWE-bench Verified | 74.8% | — |
| SciCode | — | 25.9% |
| WeirdML | 79.3% | — |
| LMArena Coding | — | 1443 |

## Agentic & Tool Use

- GPT-5.3 Codex: 47.8 (#9)
- MiMo-V2-Flash: —

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |

## Reasoning

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 24.1 (#155)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| CritPt | — | 0% |
| LMArena Hard Prompts | — | 1420 |
| Epoch Capabilities Index | 156.84 | — |

## Math

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 35.6 (#141)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Math | — | 1396 |

## Knowledge

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 39.1 (#126)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Expert | — | 1425 |

## Multilingual

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 51.1 (#109)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | — | 1391 |
| LMArena Chinese | — | 1463 |
| LMArena French | — | 1429 |
| LMArena German | — | 1395 |
| LMArena Japanese | — | 1324 |
| LMArena Korean | — | 1358 |
| LMArena Russian | — | 1387 |
| LMArena Spanish | — | 1420 |

## Instruction Following

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 73.7 (#115)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Instruction Following | — | 1392 |

## Long Context

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 43.0 (#106)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | — | 1409 |

## Writing & Preference

- GPT-5.3 Codex: —
- MiMo-V2-Flash: 59.9 (#101)

| Benchmark | GPT-5.3 Codex | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | — | 1411 |
| LMArena Creative Writing | — | 1375 |
| LMArena Multi-Turn | — | 1404 |

## FAQ

### Is GPT-5.3 Codex better than MiMo-V2-Flash?

GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 40.8 on the Noometry Index. MiMo-V2-Flash costs 27× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.3 Codex or MiMo-V2-Flash?

MiMo-V2-Flash is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.

### Is GPT-5.3 Codex or MiMo-V2-Flash better for coding?

GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 35.8 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.3 Codex does, with 400K tokens against 262K.

### How many benchmarks do GPT-5.3 Codex and MiMo-V2-Flash share?

2 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and MiMo-V2-Flash has 21.
