Codestral 2508 vs Grok 4.20

Overview: In our 12-test suite Grok 4.20 wins 6 categories, Codestral 2508 wins 0, and 6 are ties. Below we compare each test (score shown as Codestral → Grok), cite ranks, and explain practical impact.

• strategic_analysis: 2 → 5. Grok scores 5 and is tied for 1st of 54 models (tied with 25 others); Codestral scores 2 (rank 44 of 54). For tasks needing nuanced tradeoffs or numeric decision-making, Grok is substantially stronger in our tests.

• constrained_rewriting: 3 → 4. Grok wins (rank 6 of 53) vs Codestral (rank 31). Grok is better at tight-length rewrites and hard character-limited transformations.

• creative_problem_solving: 2 → 4. Grok (rank 9 of 54) produces more feasible, non-obvious ideas in our testing; Codestral lagged on originality and depth.

• classification: 3 → 4. Grok ties for 1st in classification (tied with 29 others) while Codestral sits mid-pack (rank 31). For routing or tagging pipelines Grok is more reliable in our tests.

• persona_consistency: 3 → 5. Grok is tied for 1st (with 36 others) meaning it better maintains character and resists injection in our prompts; Codestral’s 3 places it much lower.

• multilingual: 4 → 5. Grok ties for 1st (with 34 others); Codestral scores 4. For non-English quality and parity, Grok has the advantage.

Ties (both models score the same):

• structured_output: 5 → 5 (both tied for 1st). Both models reliably follow JSON/schema constraints in our testing.
• tool_calling: 5 → 5 (both tied for 1st). Both select functions and sequence tool args accurately on our tests.
• faithfulness: 5 → 5 (tied for 1st). Both stick to source material and avoid hallucination in our testing.
• long_context: 5 → 5 (both tied for 1st). Both handle 30K+ token retrieval scenarios equally well in our tests.
• safety_calibration: 1 → 1 (both rank 32 of 55). Both models are conservative in our safety calibration test results.
• agentic_planning: 4 → 4 (both rank 16 of 54). Both are comparable at goal decomposition and recovery.

Practical interpretation: Codestral’s strengths (ties at tool_calling, structured_output, faithfulness, long_context) align with real coding tasks: schema outputs, FIM and code-correction workflows will be reliable and low-latency. Grok’s clear wins in strategic_analysis, persona_consistency, creative_problem_solving and multilingual make it better for complex reasoning assistants, multi-language products, and applications needing consistent personas or creative responses. All benchmark claims above are from our internal 12-test suite and the rankings shown in the payload.

Per the payload prices (assume a 50/50 split of input/output tokens for cost estimates): Codestral 2508 costs $0.60 per 1M total tokens (input $0.30/mTok, output $0.90/mTok → $0.15+$0.45), while Grok 4.20 costs $4.00 per 1M total tokens (input $2.00/mTok, output $6.00/mTok → $1.00+$3.00). At 10M tokens/month that’s $6.00 (Codestral) vs $40.00 (Grok); at 100M it’s $60.00 vs $400.00. The priceRatio in the payload is 0.15 — Codestral is ~15% of Grok’s cost. Teams with large-volume, latency-sensitive coding workloads or tight budgets should care most about the gap; teams needing the best strategic reasoning, multilingual support, or persona consistency may accept Grok’s higher price.

Choose Codestral 2508 if you need a cost-efficient, high-throughput coding model that ties Grok on tool calling, structured outputs, faithfulness and long-context handling — ideal for FIM, code correction, test generation, and high-volume deployments where cost matters. Choose Grok 4.20 if your priority is strong strategic reasoning, multilingual parity, persona consistency, or constrained rewriting — Grok wins 6 of 12 benchmarks in our testing and ranks at or near the top for those tasks, but expect roughly 6.7x higher token costs (based on the example 50/50 input-output pricing).