Qwen3.8 2.4T A95B vs Sakana Namazu
Sakana Namazu costs 1.5x less per output token than Qwen3.8 2.4T A95B ($4.00 vs $6.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $6.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
Qwen3.8 2.4T A95B or Sakana Namazu: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3.8 2.4T A95B for a larger context window (1.05M against 262K tokens).
No benchmark has tested both yet. Qwen3.8 2.4T A95B has results on AA Coding Index, SciCode and AA Intelligence Index; Sakana Namazu has none yet. 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 Qwen3.8 2.4T A95B has a AA Intelligence Index result, 39.9.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.8 2.4T A95B has a AA Agentic Index result, 50.1.
Directional only
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Sakana Namazu
Qwen3.8 2.4T A95B $0.0050, Sakana Namazu $0.0029 per turn.
List prices
At a glance
- Coding index
- Qwen3.8 2.4T A95B not benchmarked, Sakana Namazu 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
- Qwen3.8 2.4T A95B $6.00 against Sakana Namazu $4.00 per million output tokens, 1.5x apart.
- Context window
- Qwen3.8 2.4T A95B takes 1.05M tokens, Sakana Namazu 262K.
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 Index2 boards measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| AA Coding Index | 71.9 | Not measured | Only Qwen3.8 2.4T A95B measured |
| SciCode | 54.1% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.9 | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| AA Agentic Index | 50.1 | Not measured | Only Qwen3.8 2.4T A95B measured |
| MCP Atlas | 84.5% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 93.5% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.4% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 21 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| MathArena | 31.6% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.8 2.4T A95B | Sakana Namazu | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 80.3% | Not measured | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 47 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
Sakana Namazu costs less
Repository review
50K input + 3K output tokens
Sakana Namazu costs less
Agent loop
200K input + 10K output tokens
Sakana Namazu 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 | Qwen3.8 2.4T A95B | Sakana Namazu |
|---|---|---|
| Maker | Qwen | Sakana |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | August 11, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $2.00 | $0.95 |
| Output $/Mtok | $6.00 | $4.00 |
| 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 Qwen3.8 2.4T A95B better than Sakana Namazu for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3.8 2.4T A95B for a larger context window (1.05M against 262K tokens).
Which is cheaper, Qwen3.8 2.4T A95B or Sakana Namazu?
Qwen3.8 2.4T A95B costs $2.00 per million input tokens and $6.00 per million output tokens; Sakana Namazu costs $0.95 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.8 2.4T A95B or Sakana Namazu?
Qwen3.8 2.4T A95B, with 1.05M tokens against 262K.
Has any benchmark tested both Qwen3.8 2.4T A95B and Sakana Namazu?
No benchmark has tested both yet. Qwen3.8 2.4T A95B has results on AA Coding Index, SciCode and AA Intelligence Index; Sakana Namazu has none yet. Compare them on price and context instead. Data as of October 23, 2026.