Codestral 25.08 vs ERNIE 4.5 300B A47B vs Qwen Flash
Qwen Flash comes out ahead, 69 to 47 and 43 on our weighted score, and it is the cheaper option too.
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
BaiduERNIE 4.5 300B A47B
43/100- ECI—
- Price$0.28 / $1.10
- Context131K
- Our pick
Alibaba (Qwen)Qwen Flash
69/100- ECI—
- Price$0.05 / $0.40
- Context1M
Qwen Flash is our pick
Qwen Flash is the better all-round choice, scoring 69/100 against Codestral 25.08 (47) and ERNIE 4.5 300B A47B (43). It leads on price, inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen FlashQwen Flash $0.138 · Codestral 25.08 $0.45 · ERNIE 4.5 300B A47B $0.485 per 1M tokens (3:1 blend)
- Longest contextQwen FlashQwen Flash 1,000,000 · ERNIE 4.5 300B A47B 131,072 · Codestral 25.08 128,000 tokens
- Widest inputsSame inputsCodestral 25.08: Text · ERNIE 4.5 300B A47B: Text · Qwen Flash: Text
- Self-hostingERNIE 4.5 300B A47BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Codestral 25.08 | ERNIE 4.5 300B A47B | Qwen Flash |
|---|---|---|---|---|
| Price | 50% | 64 | 62 | 87 |
| Inputs & features | 30% | 35 | 25 | 45 |
| Context window | 20% | 24 | 25 | 60 |
| Overall | 100% | 47/100 | 43/100 | 69/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Codestral 25.08Mistral AI | ERNIE 4.5 300B A47BBaidu | Qwen FlashAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.28 | $0.05 (best) |
| Output | $0.90 | $1.10 | $0.40 (best) |
| Cached input | $0.03 | — | $0.01 (best) |
| Blended (3:1) | $0.45 | $0.485 | $0.138 (best) |
| Long-context rate | Same rate | Same rate | Over 256K: $0.25 / $2.00 |
| Price source | Official Mistral API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 8,192 tokens | 12,000 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | Proprietary |
| API model ID | codestral-2508 | — | qwen-flash |
| API providers | 11 (best) | 1 | 6 |
| Released | Jul 30, 2025 | Jun 30, 2025 | Jul 28, 2025 |
| Knowledge cutoff | — | — | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Codestral 25.08$4.80
- ERNIE 4.5 300B A47B$5.00
- Qwen Flash$1.30
Which should you choose?
Which is better: Codestral 25.08, ERNIE 4.5 300B A47B or Qwen Flash?
Qwen Flash is the better all-round choice, scoring 69/100 against Codestral 25.08 (47) and ERNIE 4.5 300B A47B (43). It leads on price, inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Codestral 25.08, ERNIE 4.5 300B A47B or Qwen Flash?
Qwen Flash is cheaper at $0.05 input / $0.40 output per million tokens (official Alibaba API price). Codestral 25.08 costs $0.30 input / $0.90 output per million tokens (official Mistral API price); ERNIE 4.5 300B A47B costs $0.28 input / $1.10 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for Qwen Flash versus $0.45 for Codestral 25.08 (3.3× as much) and $0.485 for ERNIE 4.5 300B A47B (3.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral 25.08 has not been scored yet, ERNIE 4.5 300B A47B has not been scored yet and Qwen Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral 25.08, ERNIE 4.5 300B A47B and Qwen Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen Flash has the largest context window at 1,000,000 tokens, against 131,072 for ERNIE 4.5 300B A47B and 128,000 for Codestral 25.08. Maximum output per response: Codestral 25.08 up to 8,192, ERNIE 4.5 300B A47B up to 12,000, Qwen Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Codestral 25.08 accepts text; ERNIE 4.5 300B A47B accepts text; Qwen Flash accepts text. They handle the same number of input types.
Are any of these open source?
ERNIE 4.5 300B A47B publishes its weights (Apache-2.0) and can be self-hosted; Codestral 25.08 and Qwen Flash is proprietary.
Which is newer?
Codestral 25.08 is the newest, released Jul 30, 2025. Qwen Flash came out Jul 28, 2025; ERNIE 4.5 300B A47B came out Jun 30, 2025. Knowledge cutoff: Qwen Flash Apr 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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