GPT-3.5 Turbo 16k vs Kimi K2 0711
Kimi K2 0711 costs 1.7x less per output token than GPT-3.5 Turbo 16k ($2.30 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.30
GPT-3.5 Turbo 16k or Kimi K2 0711: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Kimi K2 0711 on Aider polyglot, SWE-bench Verified and LiveCodeBench (Vals AI). 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 Kimi K2 0711 has a AA Intelligence Index result, 12.7.
Directional only
Reasoning
GPQA Diamond
No call
Only Kimi K2 0711 has a GPQA Diamond result, 71.5%.
Directional only
Math
AIME
No call
Only Kimi K2 0711 has a AIME result, 62.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 0711 has a MMLU Pro result, 79.4%.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0711 has a Design Arena: websites result, 1050.
Directional only
Long documents
Listed context window
Kimi K2 0711
Kimi K2 0711 takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2 0711
GPT-3.5 Turbo 16k $0.0050, Kimi K2 0711 $0.0017 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo 16k not benchmarked, Kimi K2 0711 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
- GPT-3.5 Turbo 16k $4.00 against Kimi K2 0711 $2.30 per million output tokens, 1.7x apart.
- Context window
- GPT-3.5 Turbo 16k takes 16K tokens, Kimi K2 0711 131K.
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 Index8 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 59.1% | Only Kimi K2 0711 measured |
| SWE-bench Verified | Not measured | 71.2% | Only Kimi K2 0711 measured |
| LiveCodeBench (Vals AI) | Not measured | 70.5% | Only Kimi K2 0711 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 55.6% | Only Kimi K2 0711 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.9% | Only Kimi K2 0711 measured |
| Terminal-Bench | Not measured | 27.8% | Only Kimi K2 0711 measured |
| GSO-Bench | Not measured | 4.9% | Only Kimi K2 0711 measured |
| WeirdML | Not measured | 39.4% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.7 | Only Kimi K2 0711 measured |
| Epoch Capabilities Index | Not measured | 140.0 | Only Kimi K2 0711 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 61.1% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 71.5% | Only Kimi K2 0711 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.6% | Only Kimi K2 0711 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 7.4% | Only Kimi K2 0711 measured |
| SimpleBench | Not measured | 26.3% | Only Kimi K2 0711 measured |
| Kagi LLM Benchmark | Not measured | 64.4% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| AIME | Not measured | 62.7% | Only Kimi K2 0711 measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | Not measured | 91.0% | Only Kimi K2 0711 measured |
| AA Math Index | Not measured | 57.0 | Only Kimi K2 0711 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 57.0% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.4% | Only Kimi K2 0711 measured |
| LegalBench (Vals AI) | Not measured | 81.5% | Only Kimi K2 0711 measured |
| CorpFin (Vals AI) | Not measured | 50.4% | Only Kimi K2 0711 measured |
| TaxEval (Vals AI) | Not measured | 70.2% | Only Kimi K2 0711 measured |
| MedQA (Vals AI) | Not measured | 84.0% | Only Kimi K2 0711 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.4% | Only Kimi K2 0711 measured |
| PRBench Finance | Not measured | 38.3% | Only Kimi K2 0711 measured |
| PRBench Legal | Not measured | 36.4% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1050 | Only Kimi K2 0711 measured |
| Design Arena: UI components | Not measured | 1028 | Only Kimi K2 0711 measured |
| Design Arena: game dev | Not measured | 973 | Only Kimi K2 0711 measured |
| Design Arena: data viz | Not measured | 1018 | Only Kimi K2 0711 measured |
| Design Arena: all code categories | Not measured | 1033 | Only Kimi K2 0711 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Kimi K2 0711 | Leader |
|---|---|---|---|
| IFBench | Not measured | 41.5% | Only Kimi K2 0711 measured |
| Long context reasoning (AA-LCR) | Not measured | 53.0% | Only Kimi K2 0711 measured |
| Fiction.LiveBench | Not measured | 61.1% | Only Kimi K2 0711 measured |
| Creative writing (Lech Mazur) | Not measured | 85.6% | Only Kimi K2 0711 measured |
| CL-bench | Not measured | 11.9% | Only Kimi K2 0711 measured |
| Neither model has a result on the other 43 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
Kimi K2 0711 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0711 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0711 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-3.5 Turbo 16k | MoonshotAI Kimi K2 0711 |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | August 28, 2023 | July 11, 2025 |
| Context window | 16K | 131K |
| Input $/Mtok | $3.00 | $0.57 |
| Output $/Mtok | $4.00 | $2.30 |
| 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 GPT-3.5 Turbo 16k better than Kimi K2 0711 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo 16k or Kimi K2 0711?
GPT-3.5 Turbo 16k costs $3.00 per million input tokens and $4.00 per million output tokens; Kimi K2 0711 costs $0.57 and $2.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo 16k or Kimi K2 0711?
Kimi K2 0711, with 131K tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo 16k and Kimi K2 0711?
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Kimi K2 0711 on Aider polyglot, SWE-bench Verified and LiveCodeBench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.