GPT-5.3 Codex vs MiMo-V2.5
Head-to-head specifications
| Metric | GPT-5.3 Codex | MiMo-V2.5 | Difference |
|---|---|---|---|
| Intelligence Index | 44.0 | 37.0 | +18.9% |
| Context window | 922K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.05 | $0.06 | +1,650.0% |
| Output speed (tokens/s) | 85 | 83 | +2.4% |
| Access | Proprietary API | Open weights | — |
- GPT-5.3 Codex leads overall capability (Intelligence Index 44.0 vs 37.0).
- MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 17.5× cheaper.
- MiMo-V2.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.3 Codex or MiMo-V2.5?
GPT-5.3 Codex advantages
- General intelligence (+16%)
MiMo-V2.5 advantages
- Context window (+8%)
- Affordability (+94%)
Which should you choose?
- Choose the GPT-5.3 Codex if you need the strongest overall reasoning and accuracy.
- Choose the MiMo-V2.5 if you work with long documents or large codebases.
Value for money
MiMo-V2.5 offers more intelligence per dollar (14.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
GPT-5.3 Codex vs MiMo-V2.5: which should you choose?
GPT-5.3 Codex — OpenAI multimodal model with an Intelligence Index of 44, a 922K-token context window and a blended price of $1.05/1M tokens.
MiMo-V2.5 — Xiaomi multimodal model with an Intelligence Index of 37, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
GPT-5.3 Codex vs MiMo-V2.5: GPT-5.3 Codex scores higher on the Intelligence Index. GPT-5.3 Codex leads overall capability (Intelligence Index 44.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 17.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.3 Codex scores 44.0 versus 37.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.3 Codex generates faster (85 vs 83 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5 is the cheaper model to run ($0.06 vs $1.05 per 1M tokens). GPT-5.3 Codex is proprietary api and MiMo-V2.5 is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GPT-5.3 Codex better than the MiMo-V2.5?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.3 Codex leads overall capability (Intelligence Index 44.0 vs 37.0).
What is the main difference between the GPT-5.3 Codex and the MiMo-V2.5?
GPT-5.3 Codex leads overall capability (Intelligence Index 44.0 vs 37.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 17.5× cheaper.
Which is better value?
MiMo-V2.5 offers more intelligence per dollar (14.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the GPT-5.3 Codex if you need the strongest overall reasoning and accuracy. Choose the MiMo-V2.5 if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.