Model comparison
Seed 2.0 Pro vs GPT-5.3 Codex
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 42.5 on the Noometry Index. Seed 2.0 Pro costs 4.3× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in coding, where GPT-5.3 Codex leads 48.5 to 42.9.
- Seed 2.0 Pro is cheaper at $0.50 / $3 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 256K.
Side by side
| Seed 2.0 Pro | GPT-5.3 Codex | |
|---|---|---|
| Provider | ByteDance Seed | OpenAI |
| Noometry Index | 42.5 | 45.5 |
| Released | 2026-02-14 | 2026-02-05 |
| Weights | Proprietary | Proprietary |
| Context window | 256K | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.50 | $1.75 |
| Output $ / M tokens | $3 | $14 |
| Results tracked | 20 | 8 |
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Category by category
Coding GPT-5.3 Codex leads
Seed 2.0 Pro: 42.9 (#84), GPT-5.3 Codex: 48.5 (#51)
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| SWE-bench Verified | — | 74.8% |
| LMArena WebDev | — | 1408 |
| WeirdML | — | 79.3% |
| LMArena Coding | 1472 | — |
| ALE-Bench | — | 1,655 |
Agentic & Tool Use Not comparable
Seed 2.0 Pro: —, GPT-5.3 Codex: 47.8 (#9)
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | — | 78.4% |
| METR Time Horizons | — | 74.5% |
| Vending-Bench 2 | — | 5,940 |
Reasoning Not comparable
Seed 2.0 Pro: 23.1 (#167), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| NYT Connections (extended) | 28.4% | — |
| Thematic Generalization | 57.1% | — |
| LMArena Hard Prompts | 1453 | — |
| Epoch Capabilities Index | — | 156.84 |
Math Not comparable
Seed 2.0 Pro: 36.5 (#110), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Math | 1440 | — |
Knowledge Not comparable
Seed 2.0 Pro: 39.6 (#119), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Expert | 1440 | — |
Multimodal Not comparable
Seed 2.0 Pro: 40.8 (#34), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Vision | 1275 | — |
Multilingual Not comparable
Seed 2.0 Pro: 54.8 (#37), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1442 | — |
| LMArena Chinese | 1490 | — |
| LMArena French | 1470 | — |
| LMArena German | 1443 | — |
| LMArena Japanese | 1408 | — |
| LMArena Korean | 1412 | — |
| LMArena Russian | 1450 | — |
| LMArena Spanish | 1460 | — |
Instruction Following Not comparable
Seed 2.0 Pro: 74.7 (#86), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1414 | — |
Long Context Not comparable
Seed 2.0 Pro: 43.6 (#85), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
Seed 2.0 Pro: 63.0 (#66), GPT-5.3 Codex: —
| Benchmark | Seed 2.0 Pro | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1448 | — |
| LMArena Creative Writing | 1406 | — |
| LMArena Multi-Turn | 1441 | — |
Frequently asked questions
Is Seed 2.0 Pro better than GPT-5.3 Codex?
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 42.5 on the Noometry Index. Seed 2.0 Pro costs 4.3× 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, Seed 2.0 Pro or GPT-5.3 Codex?
Seed 2.0 Pro is cheaper. It lists at $0.50 per million input tokens and $3 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is Seed 2.0 Pro or GPT-5.3 Codex better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 42.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 256K.
How many benchmarks do Seed 2.0 Pro and GPT-5.3 Codex share?
0 benchmarks have published results for both models. Seed 2.0 Pro has 20 scored results on Noometry and GPT-5.3 Codex has 8.