Palmyra X5 vs Qwen3 235B-A22B Thinking 2507 vs Pixtral Large (25.02)
Palmyra X5 comes out ahead, 47 to 42 and 33 on our weighted score, though Qwen3 235B-A22B Thinking 2507 is 2.1× cheaper per token.
- Our pick
Writer
Palmyra X5
47/100- ECI—
- Price$0.60 / $6.00
- Context1M
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
Palmyra X5 is our pick
Palmyra X5 is the better all-round choice, scoring 47/100 against Qwen3 235B-A22B Thinking 2507 (42) and Pixtral Large (25.02) (33). It leads on inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on price. 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 priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · Palmyra X5 $1.95 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextPalmyra X5Palmyra X5 1,000,000 · Qwen3 235B-A22B Thinking 2507 262,144 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsPalmyra X5 and Pixtral Large (25.02)Palmyra X5: Text, Images · Qwen3 235B-A22B Thinking 2507: Text · Pixtral Large (25.02): Text, Images
- Self-hostingQwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Palmyra X5 | Qwen3 235B-A22B Thinking 2507 | Pixtral Large (25.02) |
|---|---|---|---|---|
| Price | 50% | 35 | 49 | 26 |
| Inputs & features | 30% | 60 | 35 | 50 |
| Context window | 20% | 60 | 37 | 24 |
| Overall | 100% | 47/100 | 42/100 | 33/100 |
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 | Palmyra X5Writer | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Pixtral Large (25.02)Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.8 | — |
| ECI rank | — | #93 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 80.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% | — |
| SimpleQA VerifiedShort factual questions | — | 40.4% | — |
| Price per million tokens | |||
| Input | $0.60 | $0.30 (best) | $2.00 |
| Output | $6.00 | $2.90 (best) | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $1.95 | $0.95 (best) | $3.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 11 providers | Median of 3 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 262,144 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 81,920 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Proprietary |
| API model ID | — | — | — |
| API providers | 4 | 12 (best) | 3 |
| Released | Apr 28, 2025 | Jul 25, 2025 | Apr 8, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Palmyra X5$18.00
- Qwen3 235B-A22B Thinking 2507$8.80
- Pixtral Large (25.02)$32.00
Which should you choose?
Which is better: Palmyra X5, Qwen3 235B-A22B Thinking 2507 or Pixtral Large (25.02)?
Palmyra X5 is the better all-round choice, scoring 47/100 against Qwen3 235B-A22B Thinking 2507 (42) and Pixtral Large (25.02) (33). It leads on inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on price. 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, Palmyra X5, Qwen3 235B-A22B Thinking 2507 or Pixtral Large (25.02)?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). Palmyra X5 costs $0.60 input / $6.00 output per million tokens (median across 4 API providers); Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.95 for Palmyra X5 (2.1× as much) and $3.00 for Pixtral Large (25.02) (3.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Palmyra X5 has not been scored yet, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and Pixtral Large (25.02) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Palmyra X5, Qwen3 235B-A22B Thinking 2507 and Pixtral Large (25.02) 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?
Palmyra X5 has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 128,000 for Pixtral Large (25.02). Maximum output per response: Palmyra X5 up to 8,192, Qwen3 235B-A22B Thinking 2507 up to 81,920, Pixtral Large (25.02) up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Palmyra X5 accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text; Pixtral Large (25.02) accepts text and images. Palmyra X5 handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Palmyra X5 and Pixtral Large (25.02) is proprietary.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 2025. Palmyra X5 came out Apr 28, 2025; Pixtral Large (25.02) came out Apr 8, 2025.
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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