Seed-2.0-Code vs GPT-4
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $3.00
- Coding
- No data
- Overall
- 6.7
- Output $/M
- $60.00
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Seed-2.0-Code has the edge where output tokens cost 20.0x less, and where the context window is larger, 262K against 8K tokens.
No benchmark has tested both yet. Seed-2.0-Code has none yet; GPT-4 on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set). Compare them on price and context instead.
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
No call
Only GPT-4 has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Seed-2.0-Code
Seed-2.0-Code takes 262K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Seed-2.0-Code
Seed-2.0-Code $0.0020, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- Seed-2.0-Code not benchmarked, GPT-4 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Seed-2.0-Code $3.00 against GPT-4 $60.00 per million output tokens, 20.0x apart.
- Context window
- Seed-2.0-Code takes 262K tokens, GPT-4 8K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 22.4% | Only GPT-4 measured |
| SWE-bench Lite | Not measured | 39.7% | Only GPT-4 measured |
| SWE-bench (full test set) | Not measured | 12.5% | Only GPT-4 measured |
| AA Coding Index | Not measured | 13.1 | Only GPT-4 measured |
| LMArena Text: coding | Not measured | 1209 | Only GPT-4 measured |
| LMArena Text: software and IT | Not measured | 1195 | Only GPT-4 measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only GPT-4 measured |
| Epoch Capabilities Index | Not measured | 125.7 | Only GPT-4 measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 34.9% | Only GPT-4 measured |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | Not measured | 1200 | Only GPT-4 measured |
| LMArena Text: expert | Not measured | 1149 | Only GPT-4 measured |
| LMArena Text: hard prompts in English | Not measured | 1209 | Only GPT-4 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | Not measured | 1230 | Only GPT-4 measured |
| LMArena Text: mathematical work | Not measured | 1228 | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 56.2% | Only GPT-4 measured |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | Not measured | 1177 | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1145 | Only GPT-4 measured |
| LMArena Text: legal and government | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: business and finance | Not measured | 1145 | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | Seed-2.0-Code | GPT-4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 33.2% | Only GPT-4 measured |
| DTBench | Not measured | 62.7% | Only GPT-4 measured |
| LMCA | Not measured | 17.1% | Only GPT-4 measured |
| HellaSwag | Not measured | 95.3% | Only GPT-4 measured |
| WinoGrande | Not measured | 87.5% | Only GPT-4 measured |
| LMArena Text | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: instruction following | Not measured | 1201 | Only GPT-4 measured |
| LMArena Text: creative writing | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: English | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: non-English | Not measured | 1194 | Only GPT-4 measured |
| LMArena Text: Chinese | Not measured | 1184 | Only GPT-4 measured |
| LMArena Text: German | Not measured | 1198 | Only GPT-4 measured |
| LMArena Text: Japanese | Not measured | 1137 | Only GPT-4 measured |
| LMArena Text: Korean | Not measured | 1087 | Only GPT-4 measured |
| LMArena Text: French | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: Spanish | Not measured | 1197 | Only GPT-4 measured |
| LMArena Text: Russian | Not measured | 1195 | Only GPT-4 measured |
| LMArena Text: Polish | Not measured | 1265 | Only GPT-4 measured |
| LMArena Text: multi-turn | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: longer queries | Not measured | 1190 | Only GPT-4 measured |
| LMArena Text: writing and language | Not measured | 1219 | Only GPT-4 measured |
| LMArena Text: entertainment and media | Not measured | 1184 | Only GPT-4 measured |
| Neither model has a result on the other 24 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
Seed-2.0-Code costs less
Repository review
50K input + 3K output tokens
Seed-2.0-Code costs less
Agent loop
200K input + 10K output tokens
Seed-2.0-Code 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 | ByteDance Seed Seed-2.0-Code | OpenAI GPT-4 |
|---|---|---|
| Maker | ByteDance Seed | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 12, 2026 | May 28, 2023 |
| Context window | 262K | 8K |
| Input $/Mtok | $0.50 | $30.00 |
| Output $/Mtok | $3.00 | $60.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Seed-2.0-Code better than GPT-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Seed-2.0-Code has the edge where output tokens cost 20.0x less, and where the context window is larger, 262K against 8K tokens.
Which is cheaper, Seed-2.0-Code or GPT-4?
Seed-2.0-Code costs $0.50 per million input tokens and $3.00 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Seed-2.0-Code or GPT-4?
Seed-2.0-Code, with 262K tokens against 8K.
Has any benchmark tested both Seed-2.0-Code and GPT-4?
No benchmark has tested both yet. Seed-2.0-Code has none yet; GPT-4 on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set). Compare them on price and context instead. Data as of October 21, 2026.